HarmAmp Benchmark and TrajSafe Monitor for Multi-Turn Harm Amplification in LLMs
This paper introduces HarmAmp, a benchmark covering twelve risk categories designed to evaluate how LLMs compound harm across multi-turn conversations, addressing two threat vectors: democratizing specialized harmful expertise and scaling harmful operations. The authors also propose TrajSafe, a proactive monitoring system that anticipates harmful conversational trajectories and intervenes by probing user intent or steering toward safer outputs. Experiments show TrajSafe reduces multi-turn harmfulness while maintaining low over-refusal rates and preserving general model capabilities. The work highlights a gap in existing safety research that focuses on single-turn evaluations rather than extended interaction dynamics.
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An Introduction to AI Secure LLM Safety Leaderboard
Hugging Face introduces the DecodingTrust-based LLM Safety Leaderboard, a benchmark framework for evaluating large language models across multiple safety and trustworthiness dimensions. The leaderboard aims to provide standardized, reproducible safety assessments covering areas such as toxicity, stereotype bias, adversarial robustness, and privacy. It offers a public ranking of models to help researchers and practitioners compare safety properties across different LLMs.
Study characterizes how mixed compliance demonstrations drive jailbreaking in safety-aligned LLMs
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Systematic Evaluation of LLM Safety Failures on Eating Disorder Queries with Clinician Feedback
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Boiling the Frog: A Multi-Turn Benchmark for Agentic Safety
Researchers introduce 'Boiling the Frog,' a multi-turn safety benchmark evaluating whether tool-using AI agents in corporate/office settings are susceptible to incremental attacks that begin with benign requests before introducing harmful payloads. The benchmark uses stateful multi-turn evaluation with a three-level operational risk taxonomy grounded in the EU AI Act and its GPAI Code of Practice. Across nine models, aggregate strict attack success rate is 44.4%, ranging from 20.5% for Claude Haiku 4.5 to 92.9% for Gemini 3.1 Flash Lite, with loss-of-control scenarios reaching 93.3% category-level ASR.
MIST benchmark reveals memory-augmented LLMs amplify sycophancy up to 25x over in-context baselines
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PsychoSafe: Framework for Psychologically-Informed LLM Refusals in High-Risk Interactions
Researchers introduce PsychoSafe, a refusal framework that reframes LLM non-compliance as structured supportive communication grounded in evidence-based psychological intervention strategies. The work constructs an 8,019 prompt-response corpus across five risk domains and applies prompting and parameter-efficient fine-tuning to Qwen 3.5 27B, achieving 28.1% improvement in refusal quality over a generic baseline with notable gains in resource referral and psychological grounding. Evaluations on SORRY-Bench and XSTest reveal strong in-domain robustness but limited out-of-domain generalization, pointing to a need for more diverse fine-tuning data. The framework is relevant to safety alignment work targeting crisis, coercion, and escalating-intent scenarios.
Systematic evaluation of LLM prompt sensitivity in healthcare settings reveals safety risks
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