LLM Data Leakage: How Sensitive Data Escapes Without Anyone Noticing
Most conversations about AI security focus on attacks. Prompt injection. Jailbreaks. Model misuse. These risks are real, but they tend...
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Why AI Security Testing Must Be Continuous (Not One-Time)
For a long time, application security operated under a simple assumption: once an application passed security checks before release, its...
The Hidden Attack Surface of LLM-Powered Applications
Large language models have moved well beyond experimental chatbots and internal productivity tools. Today, they sit inside production systems, power...
How to Configure Bright MCP in Augment Code
This page will guide you on how to setup Bright’s MCP in Augment Code
Beyond the Sandbox: Advanced Techniques for LLM Red Teaming
When I first started testing large language models, the work felt deceptively simple. Red teaming looked like a lock-and-key problem:...
Prompt Injection Attacks: Why Traditional AppSec Tools Fall Short
Prompt injection is often treated as a lightweight issue. In many reviews, it gets grouped under generic “input validation” concerns...
The 2026 State of LLM Security: Key Findings and Benchmarks
Large language models have moved well beyond experimental deployments. In 2026, LLMs are embedded across customer-facing products, internal platforms, development...
Prompt Injection vs. Data Poisoning: The Two Biggest LLM Threats
Large Language Models are rapidly becoming a core layer of modern software systems. They are no longer limited to chat...
Model Context Protocol Security: Identifying and Preventing MCP Vulnerabilities
As large language models continue to move from experimental use cases into core production systems, the infrastructure surrounding them has...
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