arXiv AI
In Vino Veritas and Vulnerabilities: Examining LLM Safety via Drunk Language Inducement
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arXiv AI
Aug 20
Jailbreaking in the Haystack
The paper "Jailbreaking in the Haystack" introduces NINJA, a jailbreak technique that exploits long-context language models by appending benign, model-generated content to harmful user goals. It demonstrates that the position of harmful goals within the context is crucial for safety, and shows that NINJA significantly boosts attack success rates on models such as LLaMA, Qwen, Mistral, and Gemini. Unlike previous methods, NINJA is low-resource, transferable, less detectable, and compute‑optimal, revealing that carefully crafted benign long contexts can expose fundamental vulnerabilities in modern LMs.
Hugging Face Trending Papers
Sep 3
IndicSafeEval: Safety Robustness of Large Language Models under Multilingual Persuasive Jailbreak Attacks
IndicSafeEval is a new evaluation framework that tests the safety robustness of large language models against persuasion-based jailbreak attacks in Indian languages. The benchmark covers ten safety-critical content categories, six persuasive strategies, and four languages—Hindi, Bengali, Marathi, and Punjabi—producing 7,200 adversarial prompts. Experiments show that model safety varies significantly across languages, prompt styles, and risk categories, highlighting gaps in current English-centric safety assessments.
arXiv AI
Aug 10
Minimal, Local, Causal Explanations for Jailbreak Success in Large Language Models
arXiv:2605. 00123v3 Announce Type: replace Abstract: Safety trained large language models (LLMs) can often be induced to answer harmful requests through jailbreak prompts.
arXiv Computation and Language
Sep 22
BreakFun: Jailbreaking LLMs via Object Instantiation under Simulated Code Execution
arXiv:2510.17904v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are widely used because they process structures, syntax and code well, but this same ability also makes them par...
arXiv AI
Sep 7
TIER: Threat Implicitness Benchmark for Evaluating LLM Safety Behaviors
The paper introduces TIER, a Threat Implicitness Benchmark designed to evaluate large language model (LLM) safety behaviors across four risk domains and four threat levels, ranging from explicit harmful requests to sophisticated jailbreaks. Responses are scored on a six-label behavior scale by two independent LLM judges. Experiments on six open-weight LLMs reveal that safety behaviors change gradually with threat level, contextual prompts produce the most varied responses, and jailbreaks expose significant robustness gaps, underscoring the importance of behavior-aware safety evaluation.
arXiv AI
Sep 4
IndicSafeEval: Safety Robustness of Large Language Models under Multilingual Persuasive Jailbreak Attacks
IndicSafeEval is a new evaluation framework that tests the safety robustness of large language models against persuasion-based jailbreak attacks in Indian languages. The benchmark includes 7,200 adversarial prompts covering ten safety-critical content categories, six persuasive strategies, and four languages (Hindi, Bengali, Marathi, Punjabi). Experiments show that model safety varies significantly across languages, prompt styles, and risk categories, revealing that current English-centric safety evaluations miss important multilingual vulnerabilities.