Fuzzing is the art of automatic bug detection. The goal of fuzzing is to stress the application and cause unexpected behavior, resource leaks, or crashes.
The process involves throwing invalid, unexpected, or random data as inputs at a computer. Fuzzers repeat this process and monitor the environment until they detect a vulnerability.
Threat actors use fuzzing to find zero-day exploits – this is known as a fuzzing attack. Security professionals, on the other hand, leverage fuzzing techniques to assess the security and stability of applications.
This is part of an extensive series of guides about machine learning.
Why are the World’s Biggest Companies Implementing Fuzz Testing?
Some of the world’s biggest and most respected organizations are implementing fuzzing as part of their quality control and cybersecurity operations:
Google uses fuzzing to check and protect millions of lines of code in Chrome. In 2019, Google discovered more than 20,000 vulnerabilities in Chrome via internal fuzz testing.
Microsoft uses fuzzing as one of the stages in its software development lifecycle, to find vulnerabilities and improve the stability of its products.
The US Department of Defence (DoD) issued a DevSecOps Reference Design and a Application Security Guide which both requires fuzz testing as a standard part of software development processes.
These and many other organizations are adopting fuzzing into their standard development processes for several reasons:
Fuzzing does not just identify the problem, it also shows the cause of the problem and how an attacker may interact with it in a real-life attack.
Fuzzing proves a vulnerability exists, identifying problems without having to sift through false positives.
Fuzzing is fully automated, and can run independently for days or even weeks, identifying more and more vulnerabilities in a system under test.
Fuzzing is highly useful for developers. The role of developers is to develop and improve product features. While traditional security tools only point out flaws, fuzzers show the result of the flaw and demonstrate the impact of solving it.
Types of Fuzzing Tools
Fuzzing tools can be grouped into four basic types.
Grammar-Based F vs. Mutuation Fuzzing
Grammer-based or mutation fuzzers are defined by the way they handle test case generation. Some fuzzers combine both approaches.
Grammar-based fuzzers generate new test cases from a supplied model. The tester defines a “grammar”, specifying the format of inputs accepted by the application, and can define which parts of the input should be fuzzed. The fuzzer uses this model to generate a large number of inputs, which are similar to legitimate inputs, but violate some of the application’s constraints.
Mutation fuzzers randomly mutate a supplied seed input object. They are not constrained by a specific model, and “go crazy” by generating large numbers of unusual inputs. This can be very successful at identifying new bugs or execution paths that may have not been specified by the user in a grammar-based fuzzer.
Black-Box vs. White-Box Fuzzing
Fuzzers can also be grouped into either black-box or white-box approaches.
Black-box fuzzers don’t have access to program artifacts and are more commonly used by cybersecurity researchers looking for vulnerabilities in commercial products. Black-box fuzzing randomly mutates program inputs and sees how the program reacts to it. It can be highly effective in finding new bugs and security issues.
White-box fuzzers by definition require access to program source code. They are commonly used by red teams working for organizations responsible for systems or by software testing groups.
White-box fuzzing involves sweeping the program and identifying conditional branches and constraints on inputs. The fuzzer then systematically violates each of the constraints and evaluates the response.
This is a very comprehensive process that, in theory, can access all possible execution paths of the program. It can usually discover more bugs than a black-box approach, but is lacking in that it does not test the software from an external, attacker perspective.
How Does Application Fuzzing Work?
As we established above, fuzzing software is a great tool capable of finding zero-day vulnerabilities, but how does a fuzzer work?
1. Generating Test Cases
First, test cases are generated. Each security test case can be generated as a random, or semi-random data set, and then sent as input to the application.
The data set can be either generated in conformance to the format requirements of the system’s input, or as a completely malformed chunk of data the system was not meant to understand or process.
What do you think would happen to an application if negative numbers, null characters, or even special characters, were sent to some input fields? Do you know how your application would behave?
2. Interfacing with the Target to Deliver the Input
While fuzz testing, a fuzzer can interface with an application, a protocol, or a file format. While doing that, a fuzzer sends test cases to the target over the network or via a command-line argument of a running application.
Imaginative use cases can reveal ways to expose a relevant piece of code with the right specific data.
3. Monitoring the System to Detect Crashes
The success of a fuzz test is measured by the ability to confirm the impact that a fuzzer has on the targeted application.
Bright: Fuzz Testing for Application Security
Bright is the world’s first AI-Powered Application Security Fuzz-testing tool.
Bright offers the combination of the world’s leading DAST solution and a self-evolving, adaptive-learning fuzzer solution. Bright applies evolution strategies and reinforcement learning to extensively analyze the response of the application and the context of a given attack surface breaking the assumed scope of the target. Bright reports vulnerabilities that are invisible to other, unintelligent fuzz testing tools.
Bright combines different technologies to raise efficiency and performance as the most comprehensive, reliable, and accurate solution. Brightcomes with zero false-positives.
Learn more about Bright Dynamic Application Security Testing
Types of Fuzzing: Mutation, Generation, and Grammar-Based
When people ask what application fuzzing is, they usually want to know how it works in life. The answer usually starts with the types of application fuzzing.
Application fuzzing has types. Mutation-based application fuzzing is a common type of application fuzzing. It takes input and changes it a little. Like changing characters or adding weird values. To see how the application reacts to this new input. Generation-based application fuzzing is different. It makes inputs from scratch using predefined rules. This type of application fuzzing is more controlled. It also needs more setup.
Then there is grammar-based application fuzzing. This type of application fuzzing is useful for formats like XML or JSON. It knows the structure. Makes inputs that are technically good but still unusual. Each type of application fuzzing has its use depending on how the application handles input and where you think the weaknesses are in the application.
Common Application Fuzzing. Limitations
Understanding what application fuzzing is also means knowing where it does not work well. Application fuzzing is powerful. It is not a magic solution that fixes everything.
One common problem is that it does not cover everything. Application fuzzing tools might find inputs but miss deeper logic paths, especially in applications with authentication or multi-step workflows. Another challenge is that it can make a lot of noise. Application fuzzing can cause a lot of crashes or weird behavior that does not always mean there is a weakness.
There is also the problem of context. Application fuzzing tools do not always understand the business logic, so they may miss issues that only appear under certain conditions. Sometimes, setting it up becomes a barrier. Setting up effective application fuzzing takes time. So while application fuzzing is useful, it works best when used with testing approaches.
Fuzzing Tools and Frameworks You Should Know
If you are learning about application fuzzing, you will eventually learn about the tools that make it possible. There are tools, and each tool has a slightly different purpose.
For level or binary application fuzzing tools like American Fuzzy Lop are widely used. They focus on finding crashes by changing inputs. For web applications, tools like Burp Suite or OWASP ZAP have application fuzzing features that let you test parameters and endpoints.
There are also frameworks designed for APIs and structured data, where application fuzzing needs to respect formats. Some teams even build custom application fuzzing tools tailored to their applications. The choice really depends on what you are testing. Web apps, APIs, or system-level code. No single tool is good for every use case.
Interpreting Application Fuzzing Results and Reducing False Positives
Running an application fuzzer is one thing. Making sense of the results is another thing. When people ask what application fuzzing is, they often forget how much work goes into analyzing the output.
Application fuzzing can generate hundreds or even thousands of results. Not all of them are important. Some crashes are harmless while others point to weaknesses. The challenge is figuring out which one is which.
This is where validation becomes important. Of just flagging issues, teams need to confirm whether a finding is actually exploitable. Reproducing the issue, checking logs, and understanding application behavior all play a role.
Reducing positives is not about ignoring results. It is about filtering them intelligently so teams can focus on what really matters about application fuzzing.
See Additional Guides on Key Machine Learning Topics
Together with our content partners, we have authored in-depth guides on several other topics that can also be useful as you explore the world of machine learning.
Dynamic application security testing (DAST) tools provide automated security testing for various real-world threat scenarios. You can use DAST tools to identify security vulnerabilities in running applications, and remediate them so external threat actors cannot exploit them.
Unlike white-box testing, which involves getting access to the source code, DAST takes a black-box approach, emulating an external attacker. DAST tools interact with web applications and APIs and identify which vulnerabilities can actually be exploited by attackers. They can then provide actionable insights to developers to help them remediate those vulnerabilities.
DAST software can help you identify security weaknesses and fix them, ideally before attackers can exploit them to hack your application. Here are several threats you can identify using DAST tools:
SQL injection (SQLi)—a web-based attack that enables threat actors to gain access and control over a web application database. Threat actors achieve this by inserting arbitrary SQL code into a database query.
Cross-site scripting (XSS)—this vulnerability enables threat actors to inject malicious code into a web application. Once they’re in, threat actors can steal session cookies, user credentials, or other sensitive information.
eCommerce attacks—threat actors look for vulnerabilities in eCommerce platforms and content management systems (CMS) that provide easy targets. They try to stay in for a long time to reach as many targets as possible.
Successful attacks on web applications can result in information theft, especially when the breach goes undetected. Threat actors can exploit web application vulnerabilities to gain unauthorized access to personally identifiable information (PII) and credit card information.
DAST tools provide visibility into potential weaknesses and application behaviors that threat actors can exploit. These tools aim to provide you with this information before threat actors can discover and capitalize on these vulnerabilities.
Bright Security tests every aspect of your apps. It enables you to scan any target, including web applications, internal applications, APIs (REST/SOAP/GraphQL), and server side mobile applications. It seamlessly integrates with the tools and workflows you already use, automatically triggering scans on every commit, pull request or build with unit testing. Scans are blazing fast, enabling Bright to work in a high velocity development environment.
Instead of just crawling applications and guessing, Bright interacts intelligently with applications and APIs. Our AI-powered engine understands application architecture and generates sophisticated and targeted attacks. By first verifying and exploiting the findings, we make sure we don’t report any false positives.
Seamlessly integrates with existing tools and workflows—works with your existing CI/CD pipelines. Trigger scans on every commit, pull request or build with unit testing.
Spin-up, configure and control scans with code—one file, one command, one scan with no need for UI-based configuration.
Super-fast scans—interacts with applications and APIs, instead of just crawling them and guessing. Scans are made faster by an AI-powered engine that can understand application architecture and generate sophisticated and targeted attacks.
No false positives—uses AI analysis and fuzz testing to avoid returning false positives, so developers and testers can focus on releasing code.
OWASP ZAP (Zed Attack Proxy) is an open source web application security scanner. It is suitable both for experienced penetration testers and developers and QA testers who do not have security expertise.
ZAP is now the most active project maintained by OWASP, with thousands of individual contributors. It is available in 29 languages on Linux, Windows, and Mac. It also acts as a proxy server to handle HTTP/S requests, and includes a daemon mode that can be controlled using a REST API.
Nikto is an open source web server scanner that can check for:
Currently installed web server software
6700 potentially dangerous files on a web server
Old versions of 1250 server packages
Version-specific issues on 270 server packages
Misconfigurations such as multiple index files, content delivered over HTTP
The project is actively maintained with new scan items and plug-ins updated regularly. A downside is that this tool is not stealthy and scans might be blocked by IPS/IDS systems. For a more realistic test, you can try combining this tool with LibWhisker to circumvent IDS.
Nuclei provides security scanning for web protocols such as TCP, DNS, HTTP, SSL, File, Whois, and Websocket. It uses a flexible templating engine that lets it conduct a variety of security checks. Because the tool sends requests based on templates, it can enable fast scans across many hosts with no false positives.
Nuclei has a repository of vulnerability templates contributed by over 300 security researchers. These include:
1114 templates for specific CVEs
454 templates for LFI vulnerabilities
351 templates for XSS vulnerabilities
281 templates for RCE vulnerabilities
246 templates for testing vulnerable WordPress plugins
ThreatMapper automatically detects, identifies, and queries cloud-based infrastructure. It works with compute instances in public clouds, Kubernetes nodes, and serverless resources, helping to discover cloud native applications and containers and map their topology in real time. The tool can help discover and visualize attack surfaces in cloud native workloads.
Key features include:
Scanning build artifacts for vulnerabilities during builds and integrating with CI/CD pipelines.
Prioritization of vulnerabilities based on CVSS scores
Scanning container registries for vulnerable containers before deployment.
Scanning production environments for host, container, and application vulnerabilities.
Discovering production applications, including complex microservices applications, and mapping their topology.
Continuous scanning of production systems to identify new vulnerabilities.
Scanning hosts and containers and recommending how to harden configuration.
Capture and archive network traffic, including TLS decryption.
Conclusion
While there are many solutions out there, Bright Security is at the forefront of DAST technology. We have raised a $20 million funding round to continue pioneering the field, helping secure apps and APIs, without slowing down software development processes.
DAST vs Penetration Testing: What Is the Difference
What Is DAST?
What Is Penetration Testing?
Dynamic Application Security Testing (DAST) is a solution used to analyze web applications at runtime to identify security vulnerabilities and misconfigurations. DAST tools provide an automated way to scan running applications and try to attack them from a hacker’s perspective. They can then offer valuable insights into how applications are behaving, identify where hackers can launch attacks, and provide actionable guidance on how to remediate vulnerabilities. DAST tools take a black box approach to testing. They run outside the application without having access to its source code or internal architecture. DAST can be used to identify and resolve all common web application vulnerabilities including broken access control, cross-site scripting (XSS), SQL Injection (SQLi), and cross site request forgery (CSRF).
Penetration testing (also called pentesting) is a cybersecurity technique used by organizations to identify, actively exploit, and remediate vulnerabilities in applications and their security controls. Penetration tests are usually conducted by ethical hackers, who can be internal employees or contractors of an organization. Ethical hackers use the same tactics and behaviors as real hackers to assess how an organization’s computer systems, networks, or web applications could be attacked. Organizations can use the resulting report of a penetration test to discover and remediate vulnerabilities, and for compliance purposes. Ethical hackers are security professionals who use a variety of methods, tools, and techniques to simulate cyberattacks against an organization. The term “penetration” refers to the degree to which a hypothetical threat actor or hacker can break past an organization’s security measures and cause damage.
Penetration testing begins with reconnaissance. At this stage, ethical hackers spend time gathering data they use to plan their simulated attack. Based on this data they identify vulnerabilities, find a viable attack vector, gain and maintain access to the target system.
Step 2: Exploitation
The penetration testing process requires an extensive set of tools. These include network and vulnerability scanning software, as well as tools that can launch specific attacks and exploits such as brute-force attacks or SQL injections. There is also hardware designed specifically for penetration testing. For example, there are hardware devices that connect to a computer on a network and give hackers remote access to that network.
Another tool in the pentesting arsenal is social engineering. Ethical hackers might use techniques like phishing emails, pretexting (pretending to be an authority or someone known by the victim), and tailgating (entering a building immediately after an authorized person).
Step 3: Disengagement
After a penetration tester achieves access to sensitive systems and demonstrates their ability to steal data or perform other damage, they disengage, covering their tracks to avoid detection.
Step 4: Report and resolution of discovered weaknesses
The final and most important stage of a penetration test is the pentest report. This is a detailed report the ethical hacker shares with the target company’s security team. It documents the pentesting process, vulnerabilities discovered, proof that they are exploitable, and actionable recommendations for remediating them.
Internal teams can then use this information to improve security measures and remediate vulnerabilities. This can include patching vulnerable systems. These upgrades include rate limiting, new firewall or WAF rules, DDoS mitigation, and stricter form validation.
How Does DAST Work?
DAST tools go into action when an application is deployed, either in a test or staging environment or in a real production environment. They can continuously scan applications to discover new vulnerabilities or misconfigurations that are introduced over time.
Most DAST tools only test the exposed HTTP and HTML interfaces of web-enabled applications, but some also support APIs and protocols like Remote Procedure Call (RPC) and Session Initiation Protocol (SIP). DAST tools start by crawling web applications to identify URLs, forms, and other exploitable elements. A DAST tool attempts to find all the ways an application accepts input from users, testing these inputs one by one.
DAST tools can be automatically run at multiple stages of the testing and deployment process, allowing teams to quickly identify and address risks before security incidents occur. When a vulnerability is discovered, the DAST solution sends an automatic alert to the appropriate development team for the developer to fix. Some DAST solutions integrate directly with bug trackers to integrate smoothly into the development process.
DAST works best as part of a comprehensive approach to web application security testing. While DAST provides security teams with timely insight into how web applications behave in production environments, businesses often use DAST for application penetration testing and static application security testing (SAST) to discover additional vulnerabilities during early development stages.
DAST and penetration testing are often confused because of their role in helping detect application vulnerabilities. What they have in common is that both of them are black box testing techniques, which attempt to exploit vulnerabilities in applications. However, the similarities end there:
DAST uses a dynamic approach to testing web applications, while penetration testers can use both dynamic and static methods.
DAST tools are automatic, while penetration tests are usually manual (although there is a growing category of automated penetration testing tools)
DAST tools can be run at any time, enabling continuous testing and scanning of an application. Manual penetration tests are performed infrequently—typically quarterly or annually.
DAST tools are inexpensive and can typically be run as many times as needed (depending on the licensing model). Penetration tests conducted by ethical hackers are high-cost and limited to a single, well-scoped penetration test.
DAST tools can generate false positives—they might discover issues that are not real vulnerabilities. Penetration testing, by definition, does not result in false positives. However, modern DAST tools use artificial intelligence (AI) and fuzzing tools to close this gap and provide reports with zero false positives.
DAST tools can be run by anyone—security teams, developers, or even automatically with no human intervention. Pentesting requires deep expertise.
DAST tools have higher return on investment (ROI) because they can discover issues earlier in the development process. Pentesting is almost always conducted on production applications, so the cost of fixing issues is much higher.
Bright Security’s Next-Gen DAST Solution
Unlike other DAST solutions, Bright Security was built from the ground up with developers in mind. It lets developers automatically test their applications and APIs for vulnerabilities with every build.
Bright Security tests every aspect of your apps. It enables you to scan any target, including web applications, internal applications, APIs (REST/SOAP/GraphQL), and server side mobile applications. It seamlessly integrates with the tools and workflows you already use, automatically triggering scans on every commit, pull request or build with unit testing. Scans are blazing fast, enabling Bright to work in a high velocity development environment.
Instead of just crawling applications and guessing, Bright interacts intelligently with applications and APIs. Our AI-powered engine understands application architecture and generates sophisticated and targeted attacks. By first verifying and exploiting the findings, we make sure we don’t report any false positives.
3 Simple CSRF Examples to Understand CSRF for Good
What is CSRF (Cross Site Request Forgery)?
Cross-site request forgery (CSRF) is a technique that enables attackers to impersonate a legitimate, trusted user. CSRF attacks can be used to change firewall settings, post malicious data to forums, or conduct fraudulent transactions. In many cases, affected users and website owners are unaware that an attack occurred, and become aware of it only after the damage is done and recovery is not possible.
CSRF attacks exploit a mechanism that makes the sign-in process more convenient. Browsers often automatically include credentials in the request when a user tries to access a site. These credentials can include the user’s session cookies, basic authentication credentials, IP address, and Windows domain credentials.
If there is no protection against CSRF attacks, it can be easy for an attacker to hijack the session and impersonate the user. Once a user is authenticated on the site, the site cannot differentiate between a legitimate user request and a fake request sent by the attacker.
Consider a user who wants to transfer an amount of $5,000 to a family member via the web application of Acme Bank, which has a CSRF vulnerability. An attacker identifies this vulnerability and wants to intercept this transaction so that the funds are transferred to their bank account instead of to the intended recipient.
The attacker can construct two types of URLs to perform the illicit funds transfer, depending on whether the application was designed using GET or POST requests.
Forged GET request
The original request would look like something like this, transferring the amount to account #344344:
GET http://acmebank.com/fundtransfer?acct=344344&amount=5000 HTTP/1.1
The attacker’s forged request might look like this. The attacker changes the account number to their own account (#224224 in this example) and increases the transfer amount to $50,000:
Now the attacker needs to trick the victim into visiting this forged URL while signed into the banking application. The attacker might draft an email like this:
To: Victim Subject: A gift of flowers for you!
Hello victim, We know your birthday is coming up and have a special gift for you. Just click here to receive it!
The link “click here” would lead to the forged URL shown above.
Alternatively, the attacker could display a pixel within the email that fires and activates the URL if the victim enables viewing images in their email client. This is more dangerous because it requires no direct user action:
ForgedPOST request
If the banking application uses POST requests, the user’s original operation would look like this:
POST http://acmebank.com/fundtransfer HTTP/1.1 acct=344344&amount=5000
In this case, the attacker would need to craft a
SQL Injection in Python: Example and Prevention
What is SQL Injection?
SQL injection (SQLi) involves adding malicious code to a database query to gain unauthorized access to a web application’s database. Threat actors employ SQL injection techniques to manipulate SQL code, intending to execute malicious code that can help them gain access to sensitive data or compromise the database server.
A successful SQL injection attack can potentially expose any data stored by the database, including intellectual property, administrative credentials, and customer data. Threat actors can use SQL injection to target any SQL database, such as MySQL, SQL Server, and Oracle. Typically, SQL injection attacks target web applications using a database on the back end.
SQL injection is a common security exploit. Threat actors employ this technique frequently, using automated tools to increase the number of attacks they can launch and the scope of the attack. SQL injection is ranked #3 in the OWASP Top 10 lists of web application vulnerabilities.
Threat actors launch SQL injection attacks by first identifying vulnerable user inputs in a web application or page employing user input directly within an SQL query. This vulnerability allows actors to create and send input content (malicious payload) that executes malicious SQL commands in the database.
Web applications and sites typically store all data in SQL databases. SQL is a query language that manages the data stored in a relational database. SQL commands perform actions on data, allowing access, deletion, and modification. It may also allow running operating system commands. As a result, successful SQL injection attacks can lead to critical consequences.
Threat actors launch SQL injection attacks to gain unauthorized access to data and then steal it, modify it, delete it, or perform malicious actions on the breached database. For example, SQL injection can grant access to user credentials, allowing actors to impersonate database users. If the user is a database administrator, the actor gains access to all database privileges.
SQL enables authorized users to choose data and output it from the database. SQL injection vulnerabilities can allow threat actors to gain unauthorized access to all data in the database server. Threat actors can use the privileged obtained through SQL injection to modify data, add new data to the database, delete records, and drop tables.
Example of SQL Injection in Python
The following example shows a SQL injection vulnerability in a Flask application. It is based on code provided by SecureFlag.
The application defines a route for the URL /login and requests credentials from the user:
Next, the application connects to a database running on the localhost:
db = pymysql.connect("localhost") cursor = db.cursor()
This part of the application is vulnerable to SQL injection. The app runs a SQL query in which it insecurely concatenates the username and password fields:
cursor.execute("SELECT * FROM users WHERE username = '%s' AND password = '%s'" % (username, password))
If the query returns a matching record, the application logs the user in:
record = cursor.fetchone()
if record:
session['logged_user'] = username db.close()
Because the application accepts user inputs and processes them with no validation as part of the SQL query, it is possible for the attacker to switch context and override the authentication mechanism.
For example, the attacker could inject the following string into the username field:
john' OR 'a'='a';--
The following query is then submitted to the database:
SELECT * FROM users WHERE username = 'john' OR 'a'='a';-- AND password = '';
Because the ‘a’=’a’ statement is always true, the expression allows the attacker to login with the username john, if it exists, or with the first entry in the user table. The characters ;– comment out the rest of the SQL query, causing the application to ignore the password field.
The most important way to prevent SQL injection is to avoid vulnerable code and insecure coding practices. Here are a few ways to do that—they will be effective against SQL injection and many other vulnerabilities that can affect your Python code.
1. Insecure Packages
When you import a module into a Python application, the interpreter runs the code. This means you should be careful when importing modules.
The PyPi package index is a great resource, but there is no verification that all the code in libraries listed there is secure. Many malicious packages exist on PyPi, some of them attempt to trick users by adopting the names of well known libraries with small misspellings.
If you are unsure of the authenticity and contents of the outer packaging, investigate further, and if you are still unsure about its origin or security status, don’t use it.
2. Identifying Vulnerabilities
The first step in preventing vulnerabilities is to create a checklist of security best practices and review it before releasing your code or promoting it to a test environment. You should adhere to these best practices at the development stage, and automatically verify them at the testing stage. Ideally, you should adopt automated tools that scan your code at all stages of the software development lifecycle (SDLC).
3. Use Linters and Static Analysis Tools
Linters are tools that provide automated recommendations about good coding practices. They are a simple form of static application security testing (SAST) tools, which analyze source code during the development phase of a project. Linters can be used manually in the editor, as part of a local development process, or as part of an automated testing process.
There are several linters in Python, including:
Pylint—Python’s de facto linter, which emphasizes bad code practices, some of which can lead to vulnerabilities. However, it does not provide extensive security recommendations.
Bandit—you can use this tool to discover common security issues in Python code. Bandit processes each file, creates an AST node, and runs the appropriate plugin to test it.
Python IDEs like PyCharm and Wingware have these tools and others will be built in, as well as plugins for text editors that can provide security guidance.
Dynamic Analysis Security Testing Tool, or DAST Testing, is an application security solution that helps web developers discover specific vulnerabilities while running in staging or production environments.
DAST testing can find a wide range of vulnerabilities, including I/O validation issues that can make applications vulnerable to SQL injection attacks. The major benefit of DAST testing is that it can validate that a vulnerability is really exploitable—meaning that attackers can actually perform a successful SQL injection attack. DAST testing can also help identify misconfigurations and errors that could lead to a SQL injection attack.
DAST Testing for Python Applications with Bright Security
Bright Security helps automate the detection and remediation of many vulnerabilities including SQLi, early in the development process, across web applications and APIs.
By shifting DAST scans left, and integrating them into the SDLC, developers and application security professionals can detect vulnerabilities early, and remediate them before they appear in production. Bright Security completes scans in minutes and achieves zero false positives, by automatically validating every vulnerability. This allows developers to adopt the solution and use it throughout the development lifecycle.
Scan any web app, or REST, SOAP and GraphQL APIs to prevent SQL injection vulnerabilities – try Bright Security free.
What Is Interactive Application Security Testing (IAST)?
What is Interactive Application Security Testing (IAST)?
Interactive application security testing (IAST) solutions help detect and remediate vulnerabilities in web applications, as part of an organization’s security testing toolset.
IAST involves using dynamic testing, also known as runtime testing, to monitor application performance. IAST solutions instrument applications during runtime, using specialized sensors, to collect operational data and analyze user interactions with the application.
The IAST process can incorporate a combination of automated security tests, customized tests defined by the organization, or software composition analysis (SCA) to analyze open source components and find known vulnerabilities.
IAST tools deploy agents and sensors in the application during the post-build phase of the software development cycle. The agent works by observing the application’s performance and analyzing traffic flow. It maps external signatures or source code patterns to identify complex security vulnerabilities.
IAST tools provide a dashboard or web browser that lets you view testing reports in real-time and use customized reports that suit your CI/CD pipeline. You can also combine IAST results with other issues tracking tools.
IAST vs SAST vs DAST
Static application security testing (SAST) is a white box method that checks your code for vulnerabilities and flaws. It involves scanning code at rest and searching for known errors or an established set of rules. During the scan, a human or an automated program scans static code instruction by instruction and line by line.
Dynamic application security testing (DAST) is a black box method that checks running applications for security vulnerabilities and weaknesses. It involves looking for ways to attack the application without getting authorized access to the source code. A pentester or tool performing DAST simulates an external attack, typically by injecting or feeding malicious or faulty data to the tested software.
IAST employs both DAST and SAST techniques to test the inner workings of the source code, usually while the application is in development. IAST does not simulate an external attack and does not scan the entire codebase. Instead, DAST checks functionality at specific predefined points to achieve faster testing times. As a result, IAST does not provide complete coverage.
IAST Benefits and Drawbacks
Here are notable benefits of IAST:
Scans code in production—SAST tools often result in numerous false positives. For example, reporting a line of code can that was already addressed in another area of the code. IAST scan code in production while focusing only on issues that truly matter.
Scans code in development—IAST can help shift security checks to the left by checking specific issues during development. For example, IAST tools with IDE integration can offer quick feedback on features in development.
Quick remediation—IAST helps link issues with specific code locations. It enables developers to quickly click through an application to find specific problems and gain insights into quick remediation recommendations.
Here are notable drawbacks of IAST:
Programming-language dependent—IAST tools are often bound to specific technologies that may not fit your scenario. Additionally, some tools may require changing your code to include the vendor’s sensor modules.
Time intensive—IAST requires building and executing the tested application, which takes more time overall. If you use IDE plugins, you can leverage the quick feedback to catch issues during development. However, it can take longer when building big test suites that run on all production releases.
Does not provide complete code coverage—IAST scans only executed code to help reduce the number of false positives. It means the test does not cover all the code, including any code that was accidentally released without going through a quality assurance check.
Evaluate the following criteria when selecting an IAST solution:
Regulations and standards—IAST solutions must be able to scan for vulnerabilities and produce reports in line with the standards and regulations your organization complies with, such as GDPR, HIPAA, PCI DSS, and SOC 2.
Low false positives—an IAST solution should reduce the time needed to find and eliminate false positives. It should do so without requiring reconfiguration of the tool, custom services, or ongoing tuning.
Automated vulnerabilities identification—an IAST solution should accurately detect known vulnerabilities while your team performs functional tests. High severity bugs should create a ticket in your bug tracking system or break the build, while sending alerts to your developers.
Microservices support—microservices are a mainstream method for application development, and they introduce additional attack vectors. An IAST solution should allow you to assess multiple microservices from a single interface. Learn more in our guide to microservices security.
Ease DevOps agile workflows deployment—IAST tools must integrate into the existing DevOps pipeline and work seamlessly with standard build and testing tools.
Sensitive data tracking—IAST should help protect personally identifiable information (PII) and company IP. You should be able to automatically track sensitive information in your applications.
An Application Programming Interface, or API, allows software applications and services to communicate, exchange data, and trigger actions. APIs sit behind mobile applications, SaaS products, microservices, payment systems, third party integrations, and a growing number of AI powered applications.
API security is the practice of protecting these interfaces from unauthorized access, data exposure, abuse, and attack. It covers authentication, authorization, data protection, configuration, monitoring, vulnerability testing, and remediation throughout the API lifecycle.
The need for stronger API security has grown with the attack surface. Akamai documented more than 150 billion API attacks between January 2023 and December 2024. It also recorded 311 billion web application attacks in 2024, a 33% year over year increase. Akamai’s State of Apps and API Security 2025 research connects this growth in part to the rapid adoption of AI powered applications and the additional API exposure they create.
Securing APIs now requires more than putting authentication or a gateway in front of an endpoint. You need to know which APIs exist, determine what each user or service should be allowed to access, test how APIs behave under attack, and verify whether identified vulnerabilities can actually be exploited.
Why Is API Security Important?
Businesses use APIs to connect applications, customers, partners, services, and data. Depending on the application, an API might allow a caller to retrieve customer records, process payments, update an account, upload files, initiate transactions, or invoke internal services.
That makes APIs useful to legitimate applications and attractive to attackers.
A vulnerable API can allow attackers to:
Access another user’s or organization’s data
Abuse stolen credentials or session tokens
Modify transactions or application state
Invoke privileged functions
Scrape sensitive or proprietary information
Exhaust application resources
Manipulate business workflows
Reach vulnerable backend systems
Exploit trusted third party connections
The challenge grows as API estates become larger and more distributed.
API inventories are often less complete than organizations assume. Cloudflare found that organizations had a median of 33% more public facing API endpoints than they knew about, based on the difference between machine learning based discovery and customer supplied identifiers. Its Application Security Report research shows why shadow APIs can remain outside normal testing, monitoring, and governance.
How Is API Security Different From General Application Security?
API security is part of application security, but APIs create a different type of attack surface.
Traditional web security often worked around a relatively clear front door. Users interacted through a browser, requests followed predictable patterns, and a Web Application Firewall, or WAF, could inspect traffic for common malicious payloads.
Modern applications have many more entry points, including public REST APIs, mobile APIs, partner APIs, internal microservice APIs, GraphQL endpoints, administrative APIs, third party integrations, older API versions, and APIs used by AI applications or agents.
These interfaces also change frequently as developers add endpoints and release functionality through CI/CD. More importantly, many API attacks do not look malicious at the request level.
Consider:
GET /api/accounts/5821
The user may have a valid token. The request may follow the documented schema. Nothing in the payload looks suspicious. But if changing 5821 to another account identifier exposes another customer’s information, the application has an authorization vulnerability.
A WAF looking for known attack signatures may not see anything unusual because the weakness exists in the application’s logic. This is why modern API security requires authenticated testing, authorization checks, application context, and visibility into runtime behavior.
Authentication and Authorization Are Different Controls
Authentication and authorization are often grouped together, but they answer different questions.
Authentication asks: Who are you?
It verifies the identity of a user, service, or application. APIs commonly use OAuth 2.0, OpenID Connect, JWTs, API keys, mutual TLS, or session based authentication.
Authorization asks: What are you allowed to access or do?
A user can be properly authenticated and still have permission to access only certain objects, functions, fields, or accounts. For example, authenticating a customer does not mean that customer should be able to access every invoice in the system. The distinction matters because authorization weaknesses appear repeatedly in the current OWASP API Security Top 10.
OWASP API Security Top 10 Risks
As of 2026, the OWASP API Security Top 10 2023 remains the current edition. The framework reflects how API threats have evolved, with significant emphasis on authorization failures, resource consumption, sensitive business flows, API inventory, and dependencies on third party APIs. View the OWASP API Security Top 10 2023
OWASP API Security Risk
What It Means
API1:2023 Broken Object Level Authorization
A user can access an object or record they should not have permission to reach.
API2:2023 Broken Authentication
Authentication weaknesses let attackers impersonate legitimate users or services.
Users can read or change individual object properties outside their permission level.
API4:2023 Unrestricted Resource Consumption
Attackers consume excessive compute, bandwidth, storage, memory, or third party resources.
API5:2023 Broken Function Level Authorization
Users can invoke functions intended for a more privileged role.
API6:2023 Unrestricted Access to Sensitive Business Flows
Attackers automate legitimate functionality in ways that damage the business.
API7:2023 Server Side Request Forgery
An API can be manipulated into requesting unintended internal or external resources.
API8:2023 Security Misconfiguration
Unsafe configurations expose the API or supporting infrastructure.
API9:2023 Improper Inventory Management
Forgotten, undocumented, or deprecated APIs remain exposed.
API10:2023 Unsafe Consumption of APIs
Applications place too much trust in data received from other APIs.
Broken Object Level Authorization
Broken Object Level Authorization, commonly known as BOLA, remains the number one risk in the OWASP API Security Top 10.
BOLA happens when an API uses an object identifier without verifying that the caller is authorized to access that particular object.
For example:
GET /api/orders/28741
If an authenticated user changes the ID to another valid order number and receives someone else’s order, authentication has succeeded while authorization has failed.
These vulnerabilities are particularly important because the request itself can look completely legitimate. Server side authorization should therefore be checked whenever an endpoint accepts an object identifier and accesses sensitive data or functionality.
Broken Object Property and Function Level Authorization
Authorization can also fail at the property or function level.
An API might correctly let a customer access their profile but return internal properties that should only be visible to administrators. A standard user may also be blocked from an administrative function in the UI while the underlying API endpoint remains callable.
Authorization needs to apply consistently to objects, individual properties, functions, roles, token scopes, tenant boundaries, HTTP methods, and application workflows. The client interface should never be responsible for enforcing sensitive authorization decisions.
Sensitive Business Flow Abuse
Not every API attack depends on malformed requests, injection payloads, or broken authentication.
Attackers can automate legitimate business functionality to reserve limited inventory, create large numbers of accounts, abuse referral schemes, scrape proprietary information, reuse promotions, or manipulate multi step financial processes.
Each individual request may be valid. The vulnerability becomes clear only when you examine the sequence, frequency, or business outcome.
This is why API security testing increasingly needs to cover business logic rather than treating every endpoint as an isolated technical interface.
Unrestricted Resource Consumption
API operations can consume very different amounts of infrastructure. Large uploads, complex GraphQL queries, bulk exports, report generation, database searches, image processing, third party calls, and AI model requests can all be expensive.
Rate limiting helps, but organizations may also need to control request size, concurrency, query complexity, quotas, per user usage, token usage, and third party costs. The right limit depends on what the operation actually does.
Improper Inventory Management
Security teams cannot test an API they do not know exists. Two common inventory problems are:
Shadow APIs: endpoints that operate outside the organization’s known or governed API inventory.
Zombie APIs: deprecated or outdated versions that remain accessible after development teams believe they have been retired.
Both can fall outside normal vulnerability testing and monitoring.
A useful inventory records the API owner, environment, version, authentication method, data sensitivity, exposure, dependencies, and lifecycle status.
Inventory also needs to stay current as teams deploy new routes and services. Bright explores how discovery differs from active security testing in its guide to DAST, API discovery, and runtime API security.
API Security Across REST, SOAP, and GraphQL
Different API architectures introduce different technical considerations, but the same fundamental principles remain: authenticate callers, enforce authorization, validate input, protect sensitive data, constrain resources, and test how the API behaves at runtime.
REST API Security
REST is widely used for web, mobile, SaaS, and microservice APIs. REST does not provide a complete built in security model. Security depends on how the API is implemented and deployed.
Common controls include TLS, OAuth 2.0 or another appropriate authentication mechanism, server side authorization, request and response validation, secure token handling, rate limiting, logging, API gateways, and appropriate HTTP method restrictions. API gateways can centralize authentication, routing, traffic policies, TLS, logging, and rate limits.
But they do not replace security controls inside the application. A gateway can determine whether a token is valid. The application still needs to decide whether that identity can access a specific account, modify an invoice, or call an administrative function. The same limitation applies to WAFs. They can block many common malicious payloads, but they cannot reliably detect every authorization or business logic weakness.
SOAP API Security
SOAP is a structured messaging protocol that can use security standards such as WS Security, XML encryption, XML signatures, and SAML based identity.
These mechanisms can provide strong enterprise security capabilities, while REST typically relies on security controls implemented through HTTP, identity standards, gateways, and the application itself.
Neither architecture is inherently secure. The strength of API security depends on how authentication, authorization, encryption, configuration, input validation, and testing are implemented.
SOAP services still require correct authorization, safe XML parsing, secure authentication, appropriate encryption, configuration controls, and vulnerability testing.
GraphQL Security
GraphQL lets clients define the data and relationships they want returned. That flexibility is useful, but it changes the way security controls need to work.
Important considerations include:
Query depth: Deeply nested queries can require significant server processing.
Query complexity: Two queries with similar depth can have very different computational costs.
Authorization: Permission checks should apply to individual objects, fields, and operations.
Introspection: Organizations should deliberately decide whether schema introspection needs to remain publicly available in production.
Batching and aliases: A single request may contain multiple operations, which can defeat simplistic request based rate limiting.
Resource consumption: Controls should account for actual query cost rather than only HTTP request volume. Apply query depth and complexity limits, use throttling for expensive operations, enforce reasonable execution timeouts, and apply rate limits by user or IP where appropriate. OWASP specifically recommends these controls for reducing GraphQL denial of service risk. OWASP GraphQL Security Cheat Sheet
GraphQL should also be tested for injection, broken authorization, authentication weaknesses, data exposure, insecure configuration, and business logic problems.
API Security Testing Methods
Effective API security testing needs to cover more than malicious input. It should test the complete attack surface, including API discovery, authenticated functionality, authorization boundaries, input handling, business logic, resource consumption, and multi step workflows.
Testing should also continue as APIs change rather than being limited to a point in time assessment.
Discover the API Attack Surface
Start by understanding what you actually need to test.
Sources can include OpenAPI or Swagger specifications, Postman collections, HAR files, gateway configurations, developer documentation, application traffic, service inventories, and runtime API discovery.
Compare the documented attack surface with the services and traffic that actually exist. This can uncover older versions, undocumented routes, shadow APIs, test endpoints, and interfaces that never entered the formal security process.
Discovery and security testing solve different problems.
Discovery answers: What APIs exist?
Security testing answers: How can those APIs be attacked or abused?
Organizations usually need both.
Test Authentication and Authorization
Anonymous scanning covers only part of most API attack surfaces.
Important application functionality often appears after login, which means testing needs representative identities and roles.
Useful tests include:
Can User A access User B’s data?
Can one tenant access another tenant’s resources?
Can a lower privilege role invoke administrator functions?
Can read only credentials perform write operations?
Can sensitive properties be modified?
Do expired or revoked tokens still work?
Does changing an HTTP method affect authorization?
Are token scopes enforced consistently?
Authentication itself should also be tested across login, password reset, token refresh, logout, account recovery, and service to service flows.
Tools that cannot maintain authenticated context will miss a large part of many real APIs. Authentication support is therefore an important evaluation criterion when comparing API security testing tools for CI/CD pipelines.
Test Parameters, Input Validation, and Injection
API requests contain many values clients can manipulate directly, including path parameters, query parameters, JSON or XML properties, headers, cookies, object identifiers, prices, quantities, and pagination controls.
Test how the API behaves when these values fall outside expected conditions. Input validation should verify types, ranges, lengths, formats, schemas, required fields, and unexpected properties.
APIs can also expose server side vulnerabilities such as SQL injection, NoSQL injection, command injection, LDAP injection, and template injection.
Testing should determine whether untrusted input reaches an interpreter in an unsafe way, using controlled payloads and systems where testing is explicitly authorized. Automated dynamic testing can help validate these weaknesses without relying on unnecessarily disruptive commands.
Fuzzing and boundary tests can supplement this process by sending unexpected types, very large values, null values, malformed data, invalid encodings, oversized payloads, or duplicate parameters.
Look for server errors, inconsistent validation, data exposure, crashes, or abnormal resource consumption.
Test HTTP Methods and Business Logic
APIs may expose different behavior through GET, POST, PUT, PATCH, DELETE, and other methods.
Verify that methods that should be unavailable are blocked and that authorization remains consistent between methods. A user may not see a delete option in the interface while the underlying DELETE endpoint remains accessible.
Security testing should also examine complete business workflows.
Consider an order process:
Create an order
Apply a discount
Authorize payment
Confirm the order
Trigger fulfillment
Every endpoint may work correctly in isolation.
A vulnerability can still exist if a caller can reuse the discount, alter the price after payment authorization, skip a required step, replay fulfillment, or change identity between stages.
These are application behavior problems rather than simple input validation problems.
Integrate API Testing Into CI/CD
Point in time testing becomes outdated quickly when APIs change continuously.
New endpoints appear. Authentication flows change. Authorization rules evolve. Third party dependencies are added.
API security tests can run during development, on pull requests, in CI/CD, in staging, before release, on a schedule, after sensitive changes, and after remediation.
This moves feedback closer to the code change that introduced the issue. Bright’s guide to continuous API security testing in production explains how continuous testing can complement production monitoring.
API Testing Tools
API development and functional testing tools can support secure development, but they serve a different purpose from dedicated API security testing.
Functional tools help teams verify whether APIs behave as expected. Security testing deliberately challenges authentication, authorization, input handling, application logic, and runtime behavior to uncover exploitable weaknesses.
Postman helps teams create and organize requests, environments, collections, and automated functional tests.
OpenAPI and Swagger describe endpoints, schemas, parameters, authentication requirements, and responses. Accurate specifications also provide useful inputs for security testing.
Apache JMeter focuses primarily on performance and load testing and can help evaluate traffic behavior and resource consumption.
SoapUI supports automated functional testing for REST and SOAP services.
Karate supports API test automation, assertions, mocks, and reusable workflows.
Fiddler lets developers and security practitioners inspect and debug HTTP traffic.
Organizations selecting dedicated API security technology should additionally evaluate authenticated scanning, authorization testing, API coverage, runtime validation, CI/CD integration, business logic testing, and the reproducibility of findings.
API Security Best Practices for 2026
There is no single control that secures every API. Strong API security best practices combine preventive controls, attack surface visibility, continuous testing, and monitoring.
Maintain a Current API Inventory
Treat every API as a managed software asset.
Track the owner, version, environment, exposure, authentication method, data sensitivity, dependencies, and lifecycle status.
Deprecated APIs should have a real retirement process rather than disappearing from documentation while remaining reachable.
Discovery should supplement documentation because deployed endpoints can appear outside the formal inventory, especially in environments where teams frequently release new services or API versions.
Enforce Authorization Server Side
Authorization should be enforced for sensitive objects, fields, functions, and workflows.
Do not rely on the frontend to determine what a user can access.
The server should verify object ownership, roles, tenant membership, token scopes, requested functions, and relevant business rules before returning data or performing an action.
Use Strong Authentication and Protect Credentials
Choose established authentication mechanisms appropriate to the API and client.
OAuth 2.0 and OpenID Connect are widely used, but correct configuration remains essential. Validate token signatures, expiration, audience, issuer, and scopes. Rotate credentials and support revocation where appropriate.
API keys, secrets, and service credentials should not be embedded in public repositories or client applications where they can be extracted easily.
Apply least privilege so a compromised service credential does not automatically expose unrelated data or functionality.
Encrypt and Validate API Data
Use modern TLS for sensitive API traffic. Sensitive information at rest should also receive protection appropriate to its classification, regulatory obligations, and threat model.
Validate incoming requests against the expected schema. Check data types, lengths, formats, ranges, required fields, content types, and unexpected properties.
Response security matters too. Return only the information the client actually needs rather than exposing complete backend objects and relying on the client to hide sensitive fields.
Apply Appropriate Rate and Resource Controls
Rate limits should reflect the cost and sensitivity of the operation. Authentication requests, data exports, expensive searches, payments, file processing, GraphQL queries, and AI model calls may need stricter controls than basic read requests. Controls can consider frequency, payload size, query complexity, identity, concurrency, API key, token, and operation cost.
Use API Gateways and WAFs as Security Layers
API gateways and WAFs remain valuable. They can provide authentication, TLS, rate limiting, routing, logging, request policies, and protection against many recognizable attacks.
But they do not automatically understand whether an authenticated user should access a particular customer record or whether a valid sequence of API requests is abusing a business workflow. Application level authorization and security testing are still required.
Apply Zero Trust Principles to API Access
Internal network location should not automatically make an API request trustworthy.
Apply authentication and authorization according to the user or service identity, requested resource, application context, and action being performed.
NIST’s zero trust guidance removes implicit trust based solely on network location and focuses protection on users, services, assets, and resources. NIST SP 800-207: Zero Trust Architecture
For APIs, that means internal services need explicit identities and appropriate permissions just as external callers do.
Use Service Mesh Controls for Service to Service APIs
Microservice environments often contain large volumes of internal API traffic that never pass through a public API gateway.
A service mesh can help enforce consistent identity, mutual TLS, authorization policies, and traffic controls between services.
NIST’s cloud native zero trust guidance specifically describes API gateways, sidecar proxies, and application identity infrastructure as components that can enforce granular policies across services in hybrid and multi cloud environments. NIST SP 800-207A
A service mesh does not replace application level authorization or vulnerability testing, but it can reduce reliance on implicit trust between workloads.
Monitor API Activity
Security testing identifies weaknesses. Runtime monitoring helps detect attacks and abuse in deployed environments.
Useful telemetry can include authentication failures, authorization denials, unexpected endpoint activity, repeated object enumeration, token anomalies, unusual request volumes, administrative operations, and rate limit violations.
Testing and monitoring are complementary rather than interchangeable.
Account for AI Agents as API Consumers
AI agents add another class of machine identity to API ecosystems.
Postman’s 2025 State of the API Report surveyed more than 5,700 developers, architects, and executives. 51% of developers cited unauthorized or excessive API calls from AI agents as a top security concern.
Agents may hold credentials, call APIs at machine speed, chain operations across services, and access sensitive information without a human reviewing every request.
The fundamental controls remain familiar:
Give agents distinct identities
Apply least privilege
Narrow token scopes
Protect credentials
Set resource and cost limits
Monitor machine activity
Require additional approval for high impact operations where appropriate
Interfaces such as MCP tools should also be treated as part of the broader API attack surface when they expose business functions to AI systems.
Continuously Test and Revalidate
Finding a vulnerability is not the same as proving it is exploitable, and closing a ticket is not proof that the weakness is gone.
Dynamic security testing can exercise the running API and determine how it behaves under attack.
After developers apply a fix, retesting the affected behavior provides evidence that remediation worked.
What Continuous API Security Looks Like at Enterprise Scale
Bright’s work with a North American top 10 global bank shows how this model can work when dynamic testing is embedded into development rather than treated as an occasional assessment.
The bank operates more than 500 critical customer facing applications and more than 10,000 applications and APIs. After integrating Bright into multiple CI/CD pipelines, the organization increased testing from roughly 700 scans per month in 2023 to more than 30,000 scans per month by the end of 2024. Bright reports that the bank reduced vulnerability detection and remediation time by more than 70%.
The important point is not simply the scan volume. Dynamic testing became part of the ongoing development process rather than something teams waited months to perform through external testing.
Bright Dynamic AppSec tests running web applications, APIs, and business logic and integrates security testing with development workflows.
Bright supports API coverage using inputs such as OpenAPI specifications, Postman collections, and application traffic. Authenticated dynamic testing helps exercise functionality that would remain invisible to a scanner operating only against public endpoints.
The focus is not simply generating more vulnerability alerts.
Runtime validation helps establish whether a suspected vulnerability can actually be triggered in the running application, giving security and engineering teams stronger evidence for prioritization and remediation.
Bright STAR extends this approach around finding, fixing, and validating vulnerabilities across the development lifecycle.
As API estates continue to grow, security testing needs to keep pace with how APIs are actually developed and changed.
API security is the practice of protecting application programming interfaces, the data they process, and the functions they expose from unauthorized access, misuse, and attack. It includes authentication, authorization, encryption, request validation, API inventory, resource controls, security testing, monitoring, and remediation.
What are the most common API security risks?
The current OWASP API Security Top 10 includes Broken Object Level Authorization, broken authentication, Broken Object Property Level Authorization, unrestricted resource consumption, Broken Function Level Authorization, unrestricted access to sensitive business flows, Server Side Request Forgery, security misconfiguration, improper inventory management, and unsafe consumption of APIs.
What is API security testing?
API security testing examines how an API responds when identities, permissions, parameters, inputs, HTTP methods, and workflows are deliberately manipulated. It can uncover authorization failures, authentication weaknesses, injection vulnerabilities, resource abuse, security misconfiguration, and business logic flaws.
What is the difference between API security and API management?
API management focuses on operating APIs, including publishing, routing, analytics, versioning, developer access, and traffic management.
API security focuses specifically on protecting API data and functionality against vulnerabilities and abuse. API gateways and API management platforms provide useful security controls, but they do not replace application level authorization and security testing.
How often should APIs be security tested?
APIs should be tested throughout the software development lifecycle. Automated testing can run in CI/CD, staging, before releases, after sensitive changes, and after remediation. Periodic penetration testing and production monitoring can complement this continuous model.
Are API gateways enough to secure APIs?
No. API gateways can enforce authentication, rate limits, TLS, routing, logging, and other traffic policies. They cannot automatically identify every authorization or business logic vulnerability inside the underlying application. API security still requires application level controls and security testing.
DevSecOps Best Practices for a Stronger Security Culture
In today’s fast paced agile world, keeping data safe should be everyone’s job, with collaborative and robust security processes in place. To ensure success, especially with multiple iterations of software being pushed into production daily, we need to have a framework in place that will bring the required people, processes and tools together. One such framework is DevSecOps.
Implementing a DevSecOps framework and culture into your business will reduce the need for large-scale security fixes downstream by enabling your teams to make better decisions at the very beginning of their projects.
We will be building on our in-depth article on DevSecOps to focus on six best practices you should follow to implement a DevSecOps framework in your organization, covering
Speed and automation is crucial for DevOps. The time it takes for code to be pushed to production is imperative and security testing must be part of that workflow, without slowing you down. Developers are releasing 100 times more code than 10 years ago and are introducing security vulnerabilities at the same rate too. Manual tests simply can’t keep up with the speed at which organizations release code today and create too many bottlenecks.
Include automated security controls and innovative security scanning (like Bright’s) early and everywhere in the development lifecycle.
2. Check code dependencies
Despite growing concerns, enterprises are using open-source third-party components more than ever before. With the tight schedule and load of tasks, developers often don’t have the time to analyze all the components they use. We need an automated process for managing those components.
For DevSecOps code dependency checks are fundamental, and tools like OWASP Dependency-Check or Snyk can help you make sure you don’t use components with known vulnerabilities in them.
3. One step at a time
When implementing new tools like SAST to a CI/CD chain, security teams often turn on detection of all supported vulnerabilities, causing frustration and possible conflict with developers. The high rate of false positives with these tools can also impact developer adoption.
Start introducing the tool by setting it up so it detects only a few vulnerability categories, such as SQL Injection. This will give your developers time to get familiar with the tool and understand how it helps them detect vulnerabilities while coding. That will increase adoption of the tool, or reduce the likelihood of it being rejected out of hand.
One step at a time – by disrupting things you will just slow down developers and cause possible conflict.
4. Pick your tools carefully
When choosing the right security tools for your organization, you need to consider these main features:
Integration One of the key aspects you have to consider is the tools ability to seamlessly and easily integrate into the development pipeline, with a direct feedback loop for developers while also providing the security team and management with full visibility.
Developer First Mindset With DevOps and CI/CD, security tooling needs to be built with developers in mind – for security scanners, developers need to be able to initiate scans quickly, without leaving their existing toolset, controlling scans via code, or to have global configuration files for security governance.
With most legacy tools being built for security professionals, they can be hard to use, configure and understand the output. Ensuring the tooling truly focuses on enabling developers will maximize success, by leveraging your largest team to spearhead your security efforts
Speed and Accuracy Without automation and speed, your DevOps and CICD pipelines will either grind to a halt or be ineffective. Speed and accuracy of your security tests and findings respectively is paramount to ensure success. The need to run short, fast. incremental scoped defined tests on every build / commit is important, but the accuracy (or inaccuracy in most cases) can really set you back. Manual validation of security finings is a major bottleneck that compounds security and technical debt and makes it impossible to understand your true cyber posture and to prioritize remediation of fixes.
Ensuring your tool minimizes the number of false positives (or like in Bright’s case, removes them completely), means you can start to trust the output, create tickets and start to remediate issues immediately.
Although at number five on our list, this step should be taken, according to SANS Institute, before shifting to DevSecOps. With threat modeling, your security team will not only get a better understanding of the threats, types and sensitivities of your assets, but also what controls for protecting those assets are already in place and/or lacking. A threat modeling exercise will also help your security team to identify any gaps in your controls that need to be addressed.
Threat modeling can help you identify issues in architecture and design that other approaches might have missed. Unfortunately, threat modeling, unlike almost every other facet of DevSecOps, can’t be automated. For that reason it is often considered as a burden for DevSecOps. Organizations are worried that it could slow down the velocity of a CI/CD environment. But that doesn’t mean you should skip or ignore it.
Threat modeling is crucial for your DevSecOps efforts. It helps your developers change their perspective and look at their software as an attacker would do.
6. Secure coding training
Although historically a challenge was getting the needed management buy-in, investment and time to train development teams on secure coding practices, this is increasingly becoming more popular and rightly so.
Developers never intend to create insecure software, but organizations prioritize the speed at which new features are released and that they work properly, over being secure. Providing the right training to developers to prevent them from introducing security bugs into their code is a great way of being secure by design, especially when coupled with a dev-first security scanner.
What makes Bright Security the perfect tool for DevSecOps
Traditional (legacy) security scanners are built for security professionals, penetration testers and cyber experts, NOT developers. Bright has been built from the ground up to enhance DevSecOps with a developer first approach, enabling you to shift-left:
Bright integrates early into the SDLC: Unlike traditional DAST tools, Bright integrates early into the SDLC allowing you to detect and remediate vulnerabilities early and often
Bright tests every aspect of your apps and APIs: Bright is capable of scanning any target, whether web / internal apps, APIs (REST, SOAP, GraphQL, and more) or mobile
Bright seamlessly integrates with the tools and workflows you already use: Bright works with your existing CI/CD pipelines – it triggers scans on every commit, pull request or build with unit tests
Developers can spin up, configure and control scans with code: One file. One command. No UI needed
NO false-positive: Bright automatically validates every security finding so you don’t have. Start trusting your output
Penetration testing (pentesting), is a cybersecurity technique used by organizations to identify and remediate security vulnerabilities. Organizations hire ethical hackers to imitate the tactics and behaviors of external attacks. This makes it possible to evaluate their potential to compromise computer systems, networks, or web applications.
Organizations also use penetration testing to ensure compliance—some compliance standards and regulations require a penetration test to prove that the organization’s systems are secure.
1. Network Penetration Testing
Network penetration testing finds and exploits the most exposed vulnerabilities in network infrastructure such as servers, firewalls, and switches. This type of testing can help protect your business from common network-based attacks, such as:
Web application penetration testing is used to find vulnerabilities in web-based applications. It uses a three-step process:
Reconnaissance—discovering information about web servers, operating systems, services, resources, and more used by the web application
Discovery—finding vulnerabilities in the web applications and planning attack vectors to be used in the penetration test.
Attack—exploiting a vulnerability to gain unauthorized access to the application or its data.
Penetration testing of web applications can identify security vulnerabilities in databases, source code, and backend networks of web-based applications. It can not only identify vulnerabilities but also help prioritize them and provide solutions to mitigate them.
Wireless communications are services that allow data to move in and out of networks and must be protected from unauthorized access and data exfiltration. Wireless penetration testing is used to identify risks associated with wireless networks and evaluate weaknesses such as:
Deauthentication attacks
Misconfiguration of wireless routers
Session reuse
Unauthorized wireless devices
4. Physical Penetration Testing
If a threat actor has physical access to a server room or other sensitive facility, they can potentially compromise the entire network, which can have devastating effects on business, customers, and partnerships. Physical penetration testing can help secure an organization’s physical assets from threats such as social engineering, tailgating, and badge cloning.
Physical penetration testing finds weaknesses in physical controls such as locks, doors, cameras, or sensors, and allows the organization to quickly remediate defects.
5. Social Engineering Penetration Testing
When it comes to security, users are often considered the weakest link of the security chain, and are a common target for attackers. Social engineering penetration testing focuses people and processes in the organization and the security vulnerabilities associated with them. It is performed by ethical hackers who attempt social engineering attacks which are commonly experienced in the workplace, such as phishing, USB dropping, and spoofing.
The goal is to identify vulnerable individuals, groups, or processes, and to develop pathways for improving security awareness.
6. Client-Side Penetration Testing
Client-side penetration testing tests can uncover security vulnerabilities in software running on client computers, such as web browsers, media players, and content creation software packages (such as MadCap Flare, Adobe Framemaker, or Adobe RoboHelp). Attackers often compromise client-side software to gain access to company infrastructure.
Perform client-side testing to identify specific network attacks, such as:
Cross-site scripting attacks (XSS)
Clickjacking attacks
Cross-origin resource sharing (CORS)
Form hijacking
HTML injection
Open redirection
Malware infection
7. IoT Penetration Testing
IoT penetration testing looks for security vulnerabilities in connected ecosystems, including vulnerabilities in hardware, embedded software, communication protocols, servers, and web and mobile applications related to IoT devices.
The types of tests conducted on hardware, firmware, and communication protocol depend on the connected device. For example, some devices may require data dumping through electronic components, firmware analysis, or signal capture and analysis.
8. Mobile App Penetration Testing
Mobile application penetration testing is performed on mobile applications (excluding mobile APIs and servers), including both static and dynamic analysis:
Static analysis extracts source code and metadata and performs reverse engineering to identify weaknesses in application code.
Dynamic analysis finds application vulnerabilities while the application is running on a device or server.
9. Red Team Penetration Testing
Red team penetration is an advanced testing technique based on military training exercises. It uses an adversarial approach, allowing organizations to challenge their security policies, processes, and plans. Blue teaming, or “defensive security,” involves detecting and withstanding red team attacks and real-life adversaries.
Red teaming combines physical, digital, and social contexts to simulate a comprehensive real-life attack scenario, making it distinct from standard penetration testing. It encompasses tasks related to the various types of penetration testing. While a standard pentest aims to identify as many vulnerabilities as possible in a set timeframe, it is typically limited by artificial restrictions such as the task scope.
Regular penetration tests are important, but they don’t provide realistic conditions, such as combined attack techniques. Red teaming allows security teams to assess the overall environment and understand how its components function together. It requires critical thinking to identify new, complex vulnerabilities.
Red team assessments are generally more time-consuming than standard penetration tests, often taking several months to complete. This complex nature makes red teaming a rare operation, viable only for large organizations.
Picking the right penetration test usually comes down to understanding where the risk actually sits today. That’s something teams often skip over while rushing to “just run a pen test.”
When a customer-facing application or API is about to go live, testing external attack paths is the obvious place to start. If the concern is internal access, growing identity complexity, or how remote users connect into the environment, infrastructure, or Active Directory, testing tends to surface more relevant issues.
Red team exercises come into play when leadership wants to understand how different weaknesses chain together, not just whether a single bug exists.
Budget and timing matter too. A full red team once a year might sound good on paper, but it won’t help much if major changes ship every month. In fast-moving environments, narrower but more frequent tests often provide better coverage. The “right” test is the one that matches how your system is actually used – not the one that looks best in a compliance report.
Penetration Testing vs Vulnerability Scanning: What’s the Difference?
Vulnerability scanning is about breadth. It looks for known issues across a large surface area and does it quickly. That’s useful for hygiene and visibility, but it stops at identification. Most scanners can’t tell you whether an issue is exploitable in your specific environment.
Penetration testing is about depth. A tester takes a smaller set of targets and tries to break them the way an attacker would. That means chaining issues, abusing logic, and working around controls instead of just flagging missing patches. Scanners tell you what might be wrong. Pen tests show you what can actually be done. Both have a place, but they answer very different questions.
Common Penetration Testing Tools by Type
Most penetration testers don’t rely on a single tool. Web application testing often involves intercepting proxies to understand requests and responses, combined with custom scripts to test edge cases that scanners miss. API testing tools are common now, especially for environments that are mostly backend-driven.
For infrastructure and network testing, tools that enumerate services, credentials, and misconfigurations are standard, but a lot of the real work still happens manually. Cloud and identity testing has its own ecosystem of tools focused on permissions, trust relationships, and lateral movement. What matters more than the tool itself is how it’s used. Skilled testers spend more time thinking than clicking buttons.
Reporting Best Practices After Penetration Tests
A penetration test report should help teams fix problems, not just document that problems exist. The most useful reports explain how an issue was found, why it matters, and what makes it exploitable in that environment. Screenshots and request traces are far more valuable than generic descriptions.
Prioritization is critical. If everything is marked “high,” nothing is. Good reports separate theoretical risk from issues that enable real impact. They also avoid dumping raw tool output on engineering teams without context. When developers can clearly see the attack path, fixes happen faster. When they can’t, reports tend to get ignored.
Complementing Penetration Testing with Dynamic Application Security Testing (DAST)
Bright Security significantly improves the application security pen-testing progress. By providing a no-false positive, AI powered DAST solution, purpose built for modern development environments the pen-testing process can be automated and vulnerabilities can be found faster and at a lower cost. Moreover, integrating Bright Security into DevOps environments enables you to run DAST scans as part of your CI/CD flows to identify a broad set of known (7,000+ payloads) security vulnerabilities early in the development process.
In addition to detecting technical vulnerabilities, Bright Security’s unique ability to detect business logic vulnerabilities offers broader coverage and detection that any other automated solution.
