PI-Hunter automates red-teaming of LLM agents by generating and iteratively evolving source-aware test cases to induce retrieval of embedded malicious instructions from external environments.
Automated red teaming with goat: the generative offensive agent tester
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TurnGate identifies the critical turn in multi-turn dialogues where a response would complete hidden malicious intent, outperforming baselines on the new MTID dataset while keeping over-refusal low.
Introduces a multi-role red teaming framework using attacker and jury models that increases attack success rates by up to 7.9% on LLM faithfulness in question-answering tasks.
citing papers explorer
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PI-Hunter: Automated Red-Teaming for Exposing and Localizing Prompt Injections
PI-Hunter automates red-teaming of LLM agents by generating and iteratively evolving source-aware test cases to induce retrieval of embedded malicious instructions from external environments.
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One Turn Too Late: Response-Aware Defense Against Hidden Malicious Intent in Multi-Turn Dialogue
TurnGate identifies the critical turn in multi-turn dialogues where a response would complete hidden malicious intent, outperforming baselines on the new MTID dataset while keeping over-refusal low.
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A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation
Introduces a multi-role red teaming framework using attacker and jury models that increases attack success rates by up to 7.9% on LLM faithfulness in question-answering tasks.