JailbreakBench
jailbreakbench-fbe22b8d·2 events·first seen 26d agoAliases: JailbreakBench
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LASH: Adaptive Semantic Hybridization for Black-Box Jailbreaking of Large Language Models
LASH is a black-box jailbreak framework that adaptively composes outputs from multiple existing attack families into hybrid prompts using a genetic optimizer with a two-stage fitness function. Evaluated on JailbreakBench across six target models, LASH achieves 84.5% attack success rate (keyword-based) and 74.5% (LLM-judge) with only 30 mean target queries, outperforming five state-of-the-art baselines. The work demonstrates that no single jailbreak family dominates across models and harm categories, and that adaptive cross-strategy composition is a promising red-teaming direction. Results hold under three defense mechanisms.
Consensus-Labeled Prompt Bank for Measuring Coding-Model Compliance with Malicious-Code Requests
This paper introduces a large, consensus-labeled benchmark of 6,675 prompts drawn from eight existing corpora (ASTRA, CySecBench, AdvBench, JailbreakBench, MalwareBench, RedCode, RMCBench, Scam2Prompt) to evaluate whether coding-specialized LLMs refuse malicious requests. A key contribution is the distinction between requests for executable malicious code (4,748 prompts) versus harmful security knowledge (1,923 prompts), arguing that coding models should face a stricter refusal standard given their outputs can be directly weaponized. A five-judge consensus protocol achieves Fleiss' kappa of 0.767, providing a reliability-quantified substrate for cross-corpus compliance measurement that the field has previously lacked.