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ResearchAI SecurityRAG, Agents & Tool Abuse

AI Security // Retrieval, Agents & Tooling

RAG, Agents & Tool Abuse

This AI Security field brief frames RAG, Agents & Tool Abuse around the trust boundaries an authorized assessor needs to verify. Use it to plan safe validation, capture reproducible evidence and distinguish a meaningful control failure from an isolated technical symptom.

field noteassessment referencepublic sources

Why it matters in practice

Within AI Security, RAG, Agents & Tool Abuse changes how scope, prerequisites and evidence should be handled. Focus on the concrete asset or data flow under test, record the assumptions that make the condition exploitable and connect verified behavior to an operational consequence and remediation decision.

This note keeps RAG, Agents & Tool Abuse tied to offensive workflow: what to observe, what to prove, what usually goes wrong, and which references remain useful once an assessment moves from planning into active validation.

Primary coverage

  • Poison retrieval corpora and test whether malicious instructions survive chunking and ranking.
  • Review connector permissions, delegated tokens and over-broad tool access.
  • Test whether agents can call actions from weak evidence or fabricated confidence.
  • Check confirmation prompts, approval bypass paths and replay of sensitive actions.
  • Trace whether outputs can exfiltrate data from memory, tools, connectors or hidden context.

Assessment focus

The practical model is simple: what can the model see, what can it call, what can it change, and who believes the answer enough to act on it. Most agent abuse findings reduce to insecure orchestration with a language layer on top.

Selected public references

Related research

Compare the adjacent AI Security briefs below to test whether the same assumption, evidence pattern or mitigation boundary appears elsewhere in the domain.