AINeutralarXiv – CS AI · 4d ago7/10
🧠Researchers introduce MENTOR, a metacognition-driven framework that addresses a critical vulnerability in Large Language Models: an average jailbreak success rate of 57.8% across domain-specific risks in education, finance, and management. The framework uses self-assessment and consequential reasoning to identify model misalignments, then applies dynamic rule-based steering to substantially reduce attack success rates, outperforming existing safety alignment methods.
AIBullisharXiv – CS AI · 4d ago7/10
🧠Researchers introduce Reflector, a two-stage framework that enhances LLM safety by embedding self-reflection directly into the generation process rather than relying on surface-level alignment. The method achieves over 90% defense rates against sophisticated multi-step jailbreak attacks while improving general model performance by 5.85% on math benchmarks.
AINeutralarXiv – CS AI · 6d ago7/10
🧠Researchers have developed THRD, a training-free defense framework that detects multi-turn jailbreak attacks on large language models by tracking how safety risks accumulate across conversation turns. The system achieves 0.2-4.0% attack success rates while maintaining model utility, addressing a critical vulnerability where attackers exploit conversational dynamics rather than single prompts.
AINeutralarXiv – CS AI · May 297/10
🧠AIRGuard is a runtime security framework that protects AI agents from authority confusion attacks, where attackers manipulate untrusted context to misuse authorized tool access. The system reduces attack success rates from 36.3% to 5.5% while maintaining 76% of benign functionality, outperforming existing defense mechanisms by enforcing least-privilege authorization at execution time.
🧠 Haiku🧠 Sonnet
AIBullisharXiv – CS AI · May 277/10
🧠Researchers have developed a framework using behavioral geometry to predict which AI models are vulnerable to jailbreak attacks and efficiently transfer defensive measures across model populations. The approach achieves 94% detection accuracy while reducing evaluation probes by 98%, enabling practical security assessment across thousands of model configurations.
AIBullisharXiv – CS AI · May 127/10
🧠Researchers introduce Self-ReSET, a reinforcement learning framework that enables large reasoning models to recover from unsafe reasoning trajectories and adversarial attacks. The method addresses limitations in existing alignment approaches by using dynamic, on-policy data rather than static training sets, significantly improving model robustness against jailbreak attempts while maintaining utility.
AIBullisharXiv – CS AI · May 127/10
🧠Researchers demonstrate that many-shot jailbreak attacks on language models work by inducing progressive activation drift through implicit fine-tuning, and propose a simple defense using a single safety demonstration at inference time that counteracts this drift without requiring parameter modifications or white-box access.
AIBullisharXiv – CS AI · May 47/10
🧠Researchers introduce Sentra-Guard, a real-time defense system that detects and mitigates jailbreak and prompt injection attacks on large language models with 99.96% accuracy. The multilingual framework combines FAISS-indexed semantic embeddings with fine-tuned transformers and human-in-the-loop feedback, significantly outperforming existing defenses like LlamaGuard-2 and OpenAI Moderation.
🏢 OpenAI
AIBullisharXiv – CS AI · Apr 157/10
🧠Researchers introduce ASGuard, a mechanistically-informed framework that identifies and mitigates vulnerabilities in large language models' safety mechanisms, particularly those exploited by targeted jailbreaking attacks like tense-changing prompts. By using circuit analysis to locate vulnerable attention heads and applying channel-wise scaling vectors, ASGuard reduces attack success rates while maintaining model utility and general capabilities.
AIBullisharXiv – CS AI · Apr 147/10
🧠Researchers propose Risk Awareness Injection (RAI), a lightweight, training-free framework that enhances vision-language models' ability to recognize unsafe content by amplifying risk signals in their feature space. The method maintains model utility while significantly reducing vulnerability to multimodal jailbreak attacks, addressing a critical security gap in VLMs.
AIBullisharXiv – CS AI · Apr 107/10
🧠Researchers introduce SALLIE, a lightweight runtime defense framework that detects and mitigates jailbreak attacks and prompt injections in large language and vision-language models simultaneously. Using mechanistic interpretability and internal model activations, SALLIE achieves robust protection across multiple architectures without degrading performance or requiring architectural changes.
AIBullisharXiv – CS AI · Mar 177/10
🧠Researchers developed SFCoT (Safer Chain-of-Thought), a new framework that monitors and corrects AI reasoning steps in real-time to prevent jailbreak attacks. The system reduced attack success rates from 58.97% to 12.31% while maintaining general AI performance, addressing a critical vulnerability in current large language models.
AIBullisharXiv – CS AI · Mar 127/10
🧠OpenAI researchers introduce IH-Challenge, a reinforcement learning dataset designed to improve instruction hierarchy in frontier LLMs. Fine-tuning GPT-5-Mini with this dataset improved robustness by 10% and significantly reduced unsafe behavior while maintaining helpfulness.
🏢 OpenAI🏢 Hugging Face🧠 GPT-5
AINeutralarXiv – CS AI · May 116/10
🧠Researchers propose Multilingual Self-Distillation (MSD), a framework that transfers safety safeguards from high-resource languages like English to vulnerable low-resource languages in large language models. The method eliminates the need for expensive multilingual response data by leveraging an LLM's existing safety capabilities, demonstrating effective cross-lingual protection across diverse jailbreak benchmarks.
AIBullisharXiv – CS AI · Mar 176/10
🧠Researchers propose 'Two Birds, One Projection,' a new inference-time defense method for Large Vision-Language Models that simultaneously improves both safety and utility performance. The method addresses modality-induced bias by projecting cross-modal features onto the null space of identified bias directions, breaking the traditional safety-utility tradeoff.
AIBullisharXiv – CS AI · Mar 96/10
🧠Researchers introduce Answer-Then-Check, a novel safety alignment approach for large language models that enables them to evaluate response safety before outputting to users. The method uses a new 80K-sample dataset called Reasoned Safety Alignment (ReSA) and demonstrates improved jailbreak defense while maintaining general reasoning capabilities.
🏢 Hugging Face
