Langchain

Problem Statement You are deploying an AI agent that can read files, search the web, or call APIs on behalf of users. Before you ship it, you need to know: what happens when someone tries to make it do something it should not? Existing frameworks like OWASP LLM Top 10 cover the language model layer, but agents have attack surfaces that models do not – tool orchestration, multi-step reasoning, persistent memory, and cross-agent delegation. You need a systematic way to test these surfaces. ...

In red-team testing of AI agent frameworks, reasoning chain hijacking attacks achieved a 100% success rate against default LangChain configurations. Every single attempt to inject instructions into the agent’s chain-of-thought reasoning succeeded in altering the agent’s behavior. Why this matters Reasoning chain hijacking is different from basic prompt injection. Instead of injecting a single malicious instruction, the attacker injects a plausible reasoning chain that guides the agent through a series of “logical” steps toward the attacker’s goal. The agent follows the injected chain because it looks like its own reasoning. Default LangChain configurations have no defense against this — no chain validation, no reasoning integrity checks, no anomaly detection on thought patterns. ...