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It is argued that self-modifying coding agents must be designed to be more resilient to such attacks, and properties of the vulnerability, benchmark, model, and agent scaffolding that are sufficient to enable a benchmark poisoning attack are distill.
A formal framework for security in agentic AI is developed, built around an ideal predicate ϕ that decides, for a given ( prompt, context, result ) triple, whether the agent’s behaviour is acceptable.
A taxonomy of how different systems specify user-level permissions policies, both at the user interface and internally; derive internal policies from user input; and enforce those policies at run-time is constructed.
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