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AI Security // Surface Mapping

AI Attack Surface Primer

This AI Security field brief frames AI Attack Surface Primer 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, AI Attack Surface Primer 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 AI Attack Surface Primer 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

The coverage below turns AI Attack Surface Primer into an assessment map for AI Security: identify the relevant system boundary, select a safe validation path, preserve evidence and record uncertainty before drawing a conclusion.

Assessment focus

  • Map every source of attacker-controlled text, files and URLs that can enter context.
  • Trace whether retrieved content can outrank or distort system-level instructions.
  • Identify all tools, connectors and side effects reachable from the planner.
  • Check whether model output is consumed by code, analysts or automated actions without sanitisation.
  • Separate surprising output from reachable impact and prove the business consequence.

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.