#jailbreak-attacks News & Analysis

29 articles tagged with #jailbreak-attacks. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

29 articles

AIBearisharXiv – CS AI · Apr 136/10

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GRM: Utility-Aware Jailbreak Attacks on Audio LLMs via Gradient-Ratio Masking

Researchers introduce GRM, a frequency-selective jailbreak framework that exploits vulnerabilities in audio large language models while maintaining utility preservation. By strategically perturbing specific frequency bands rather than entire spectrums, GRM achieves 88.46% jailbreak success rates with better trade-offs between attack effectiveness and transcription quality compared to existing methods.

AIBullisharXiv – CS AI · Mar 36/105

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Co-Evolutionary Multi-Modal Alignment via Structured Adversarial Evolution

Researchers introduce CEMMA, a co-evolutionary framework for improving AI safety alignment in multimodal large language models. The system uses evolving adversarial attacks and adaptive defenses to create more robust AI systems that better resist jailbreak attempts while maintaining functionality.

AIBearisharXiv – CS AI · Mar 36/103

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JALMBench: Benchmarking Jailbreak Vulnerabilities in Audio Language Models

Researchers introduced JALMBench, a comprehensive benchmark to evaluate jailbreak vulnerabilities in Large Audio Language Models (LALMs), comprising over 245,000 audio samples and 11,000 text samples. The study reveals that LALMs face significant safety risks from jailbreak attacks, with text-based safety measures only partially transferring to audio inputs, highlighting the need for specialized defense mechanisms.

AIBearisharXiv – CS AI · Feb 276/107

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Analysis of LLMs Against Prompt Injection and Jailbreak Attacks

Researchers evaluated prompt injection and jailbreak vulnerabilities across multiple open-source LLMs including Phi, Mistral, DeepSeek-R1, Llama 3.2, Qwen, and Gemma. The study found significant behavioral variations across models and that lightweight defense mechanisms can be consistently bypassed by long, reasoning-heavy prompts.

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