Data Report: The Most Common Vulnerabilities in AI-Integrated Applications
AI-integrated applications are now part of everyday production environments. What began as experimentation with chatbots and internal assistants has evolved into systems where large language models influence authentication flows, automate business decisions, interact with internal tools, and retrieve sensitive data on demand. In many organizations, these systems are already mission-critical.
At the same time, security practices around AI have not matured at the same pace. Most application security programs are still structured around deterministic systems: code paths that behave the same way every time, inputs that can be validated syntactically, and vulnerabilities that map cleanly to known classes. AI systems break those assumptions.
This report documents the most common vulnerability patterns observed in real AI-integrated applications. These issues are not rare edge cases. They appear repeatedly across industries, architectures, and deployment models. Many of them are not detected by traditional AppSec tools, not because those tools are ineffective, but because the threat model itself has changed.
The core finding is simple: AI introduces a behavioral attack surface. Vulnerabilities increasingly emerge from how models interpret context, how they are allowed to act, and how their outputs are trusted downstream. Organizations that continue to treat AI as “just another dependency” are missing where risk actually lives.
How This Report Was Compiled
The insights in this report are based on hands-on analysis of AI-enabled applications tested under conditions that resemble real usage. The environments examined include:
- Web applications with embedded LLM features
- APIs where model output influences business logic
- Retrieval-augmented generation (RAG) systems connected to internal knowledge bases
- Internal tools and copilots used by engineering, support, and operations teams
- Agent-based systems capable of invoking tools or services
The focus was not on theoretical weaknesses or academic attacks. Instead, the emphasis was on what breaks when applications are exposed to unexpected inputs, ambiguous instructions, and adversarial interaction patterns over time.
Rather than looking for a single exploit, testing focused on observing how systems behave. This approach mirrors how real attackers probe AI systems: slowly, contextually, and with intent.
What AI-Integrated Applications Actually Look Like
In production, AI rarely exists in isolation. Most AI systems are deeply embedded in existing application stacks.
A typical setup might involve:
- A frontend interface collecting user input
- A backend assembling prompts from multiple sources
- A model generating a response or decision
- That output feeding into another service, workflow, or API
In many cases, the model is not just answering questions. It is:
- Deciding which data to retrieve
- Determining how a workflow proceeds
- Selecting which tool to invoke
- Generating content that is later parsed or acted upon
Once an application relies on model output to guide behavior, the model effectively becomes part of the system’s control logic. This is where many security assumptions quietly break.
Vulnerability Category 1: Prompt Injection and Instruction Manipulation
Prompt injection is the most frequently encountered vulnerability in AI-integrated applications. It is also one of the most misunderstood.
Unlike SQL injection or command injection, prompt injection does not exploit a parser or runtime. It exploits interpretation. Attackers manipulate how the model understands instructions, often without violating any syntactic rules.
This can happen through:
- Direct user input
- Indirect content retrieved from documents or APIs
- Chained interactions that gradually reshape model behavior
The impact varies. In some cases, the result is misleading output. In others, the model bypasses safeguards, exposes internal context, or takes actions it should never have been allowed to take.
What makes prompt injection particularly dangerous is that it often looks like normal usage. There is no malformed payload, no obvious error, and no crash. From the application’s perspective, everything is functioning as designed.
Vulnerability Category 2: Silent Data Leakage
Data leakage in AI systems rarely resembles a traditional breach. There is usually no alert, no spike in traffic, and no obvious sign that something went wrong.
Instead, leakage occurs as a side effect of how context is assembled and how outputs are generated.
Common sources include:
- Sensitive information pasted into prompts during debugging
- Overly broad retrieval queries in RAG pipelines
- Models generating verbose explanations that include internal data
- Logging systems are capturing prompts and outputs without proper controls
In many environments, prompts and responses are logged by default for observability. Over time, this creates repositories of sensitive data that were never intended to be stored long-term.
The most concerning aspect is that these leaks often feel “logical” in hindsight. The system did exactly what it was told to do. The problem is that nobody fully understood what that would mean at scale.
Vulnerability Category 3: Broken Authorization Through AI Mediation
Authorization failures take on new forms when AI is involved. Traditional access control checks may still exist at the API level, but once a model is introduced into the decision loop, those guarantees weaken.
Examples observed in practice include:
- Models summarizing or rephrasing data from restricted sources
- AI assistants answering questions they should not be able to answer
- Agents invoking internal tools without user-level authorization
In these cases, the application may never technically “violate” an access control rule. The violation occurs because the model is allowed to reason across data it should never have been exposed to in the first place.
This is especially common in internal tools, where trust assumptions are looser, and oversight is minimal.
Vulnerability Category 4: Unsafe Tool Invocation
Modern AI systems increasingly allow models to call tools, execute actions, or interact with APIs. This is where the line between “assistant” and “actor” starts to blur.
The most common problems here are not bugs in the tools themselves, but failures in how access is granted and enforced.
Observed issues include:
- Tools exposed with broader permissions than necessary
- Lack of constraints on how tools can be chained
- Insufficient validation of tool inputs generated by the model
- Minimal monitoring of tool usage patterns
Once a model can act on the system, the risk profile changes significantly. A single manipulated response can trigger actions that would normally require explicit user intent.
Vulnerability Category 5: Multi-Step Logic and Workflow Abuse
Some of the most impactful AI vulnerabilities do not appear in a single interaction. They emerge across a sequence of seemingly harmless steps.
Attackers may:
- Gradually steer conversations toward sensitive areas
- Accumulate partial context across sessions
- Exploit persistent state in agents or assistants
Each interaction looks benign. The risk only becomes visible when viewed as a whole. This makes detection extremely difficult using traditional, request-based security models.
Where These Vulnerabilities Commonly Appear
Patterns emerge when looking across environments. The highest concentration of issues tends to appear in:
- Internal AI copilots
- Support and operations tools
- Knowledge assistants connected to internal documentation
- AI-powered APIs used by multiple teams
These systems are often trusted by default and tested less aggressively than public-facing applications. That trust becomes an attack surface.
Why Traditional AppSec Struggles Here
Most AppSec tooling was built for a different era. It expects vulnerabilities to map to known classes, payloads, and signatures.
AI vulnerabilities break those assumptions:
- Static analysis cannot predict semantic interpretation
- Signature-based scanning has no meaningful payloads to match
- Point-in-time testing misses evolving prompts and data sources
- Behavioral abuse does not resemble the exploitation of a bug
As a result, many issues remain invisible until they are abused in production.
The Visibility Problem
One of the defining traits of AI vulnerabilities is their low visibility. Many issues:
- Do not trigger errors
- Do not degrade performance
- Do not look malicious
From logs alone, everything appears normal. This makes prioritization difficult and often leads to risk being underestimated until real damage occurs.
What This Data Says About AI Security Maturity
Across organizations, similar patterns repeat:
- AI features are deployed faster than security controls
- Models are trusted more than their behavior justifies
- Runtime monitoring is minimal or nonexistent
- Security reviews focus on infrastructure, not decision logic
This is not negligence. There is a lag between innovation and governance.
Practical Implications for Security Teams
Security teams that are adapting successfully tend to:
- Treat AI components as first-class attack surfaces
- Monitor inputs, context, and outputs at runtime
- Apply least privilege to data sources and tools
- Test AI behavior continuously, not just at launch
The goal is not to eliminate AI risk, but to make it observable and manageable.
Conclusion
AI-integrated applications don’t break in the same ways traditional software does. The issues discussed in this report aren’t caused by careless development or obvious configuration mistakes. They show up when models are allowed to interpret context, make decisions, and influence system behavior in ways that were never fully anticipated. It’s the combination of probabilistic outputs, shifting inputs, and implicit trust in model responses that creates risk – often quietly, and often without any clear sign that something has gone wrong.
Understanding these patterns is the first step. Addressing them requires security controls that operate where AI systems actually make decisions: at runtime, across context, and over time.
Ignoring this shift does not reduce risk. It simply delays when that risk becomes visible.
