Beyond the Prompt: Jailbreaking Function-Calling LLMs via Simulated Moderation Traces
New jailbreak method bypasses LLM safety by injecting fake moderation traces into function-calling pipelines—showing prompt-centric defences are structurally incomplete for agentic AI.
Summary written by editorial AI · Source link below
arXiv:2607.00481v2 Announce Type: replace Abstract: Jailbreak attacks remain a critical threat to the safe deployment of large language models (LLMs). While prior work has primarily studied attacks and defenses at the prompt level, we show that this prompt-centric paradigm overlooks a structural vulnerability in stateful, function-calling environments. In such applications, developer-defined schemas, structured arguments, and untrusted tool outputs are interleaved into a single shared model con
Editorial Analysis
Enterprises rolling out agentic AI must recognise that prompt-level guardrails are insufficient; system-level enforcement is needed to prevent function-calling abuse that could trigger unauthorised actions.
Mandate system-level action-allowlisting and output validation for all LLM-based tools that invoke backend functions.
AI agents with function-calling capabilities can be tricked into unsafe actions despite built-in moderation, requiring architectural security controls.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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