Papers

• Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification (2026)
  Carter Luck et al.
  5.01
• Risk Under Pressure: Compute-Aware Evaluation of Adversarial Robustness in Language Models (2026)
  Malikeh Ehghaghi et al.
  1.94
• The Hidden Risks of Large Reasoning Models: A Safety Assessment of R1 (2025)
  Kaiwen Zhou et al.
  1.28
• X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents (2025)
  Salman Rahman et al.
  1.28
• The Aloe Family Recipe for Open and Specialized Healthcare LLMs (2025)
  Dario Garcia-Gasulla et al.
  1.28
• AdvChain: Adversarial Chain-of-Thought Tuning for Robust Safety Alignment of Large Reasoning Models (2025)
  Zihao Zhu et al.
  1.28
• Position: Privacy Is Not Just Memorization! (2025)
  Niloofar Mireshghallah et al.
  1.28
• Imperceptible Jailbreaking against Large Language Models (2025)
  Kuofeng Gao et al.
  1.28
• Adaptive Attacks on Trusted Monitors Subvert AI Control Protocols (2025)
  Mikhail Terekhov et al.
  1.28
• Machine Text Detectors are Membership Inference Attacks (2025)
  Ryuto Koike et al.
  1.28
• OmniSafeBench-MM: A Unified Benchmark and Toolbox for Multimodal Jailbreak Attack-Defense Evaluation (2025)
  Xiaojun Jia et al.
  1.28
• CLINIC: Evaluating Multilingual Trustworthiness in Language Models for Healthcare (2025)
  Akash Ghosh et al.
  1.28
• On the Reliability of Watermarks for Large Language Models (2023)
  John Kirchenbauer et al.
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• Jailbroken: How Does LLM Safety Training Fail? (2023)
  Alexander Wei et al.
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• Can LLMs Follow Simple Rules? (2023)
  Norman Mu et al.
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• WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models (2024)
  Liwei Jiang et al.
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• Hallucinating AI Hijacking Attack: Large Language Models and Malicious Code Recommenders (2024)
  David Noever et al.
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