Paper says target agents criticize attacks better than they propose them
Original title: The Target Agent Is Its Own Best Critic but a Poor Proposer: Self-Improved Red Teaming
Separating criticism and proposal reached 63.2% attack success on DTAP-BENCH across four domains.
Researchers compare who should criticize failed attacks and who should write the next prompt in automatic LLM red teaming. Self-criticism using the target session's live context outperformed an external critic, while self-proposal caused refusal or sandbagging. Removing reasoning cut downstream attack success by 6% to 17%. Their preferred setup lets the target diagnose failures and a separate attacker propose the next attempt, reaching 63.2% overall ASR on DTAP-BENCH.
Unconfirmed: Success rates come from the authors' benchmark runs and are not evidence of real-world exploitation.
- DTAP-BENCH covers Telecom, Workflow, CRM, and Code.
- Tests include direct misuse and indirect threat models.
- Removing reasoning lowered attack success by 6% to 17%.
- The reported overall attack success rate is 63.2%.
From the original article
1 min read at arxiv.orgAutomatic red teaming increasingly relies on large language models (LLMs) to iteratively diagnose failed attacks and propose improved adversarial prompts or injections. Although prior work strengthens this loop through prompt engineering and search-harness design, the mechanisms underlying its two core roles, criticism and attack proposal, has not been explored.
By Mintong Kang · arxiv.org
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