The paper introduces SEAV, a verification‑centric framework for evaluating jailbreak attempts against large language models. SEAV decomposes responses into ordered steps and checks both validity and correctness using LLM‑as‑a‑judge and retrieval‑grounded verification. The method reduces false positives by 14.9 percentage points on a strategic‑dishonesty diagnostic and reclassifies 22.1–51.0% of previously successful jailbreaks as invalid across multiple benchmarks.
arXiv:2607. 19424v1 Announce Type: cross Abstract: The assessment of jailbreak attacks against large language models currently suffers from inconsistent evaluation criteria and methods, leading to unreliable estimates of attack success rates.
arXiv:2502.16903v3 Announce Type: replace Abstract: Despite the growing interest in jailbreaks as an effective red-teaming tool for building safe and responsible large language models (LLMs), flawed...
arXiv:2610.19306v1 Announce Type: new Abstract: Jailbreak attacks against large language models (LLMs) are evaluated using attack success rate (ASR), which depends on correctly identifying responses...
arXiv:2609.05850v1 Announce Type: cross Abstract: Despite the significant efforts devoted to aligning large language models (LLMs) with human values and ensuring safe deployment, recent work has reve...
arXiv:2605.27110v2 Announce Type: replace-cross Abstract: In this work, we propose BAIT (Boundary-Aware Iterative Trap), a three-step jailbreak framework that elicits malicious information through in...
The paper introduces DiagSafe, a diagnostic evaluation framework that replaces binary safe/unsafe labels with prompt-level rubrics grounded in AI safety policies. DiagSafe uses a bilingual benchmark with an 84‑label safety taxonomy, comprising DiagBase and DiagAttack subsets, to assess safety and usefulness independently. Experiments show DiagSafe achieves high agreement with human annotations (95.34%) and strong inter‑judge consistency (Cohen’s κ = 0.864), revealing significant differences in unsafe rates and vulnerabilities across leading LLMs, especially in Chinese and multi‑turn contexts.
arXiv:2606. 25750v1 Announce Type: cross Abstract: Safety evaluation of large language models (LLMs) is commonly performed by querying models with unsafe or jailbreak prompts and judging whether their outputs violate a safety policy.
The paper introduces SSRFT (Supervised Safe-Role Fine‑Tuning), a new framework that reframes safety alignment for large language models as internalizing a predefined safe role rather than relying on explicit refusal patterns. SSRFT builds a Safe‑Role Question‑Answer dataset from psychometric questions, limited jailbreak prompts, and a safe‑role description, then synthesizes and validates role‑consistent responses to expose models to diverse safety scenarios. Experiments on multiple base and instruct models demonstrate that SSRFT yields more robust and generalizable safety alignment, better resilience to prefilling attacks, improved generalization to unseen jailbreak domains, and reduced over‑refusal on benign queries while preserving general capabilities.
arXiv:2605. 00123v3 Announce Type: replace Abstract: Safety trained large language models (LLMs) can often be induced to answer harmful requests through jailbreak prompts.
MLCommons Jailbreak Benchmark v1.0 is a new methodology for assessing how well large language models resist single‑turn, text‑based jailbreak attacks. It evaluates eight open‑weight systems with 264 seed prompts across eleven hazard categories, using the AILuminate Assessment Standard v1.4 to measure the Resilience Gap—the difference in safety performance between baseline and adversarial conditions. The benchmark found that unsafe‑response rates rose from 11.08% to 18.65% under jailbreak conditions, yielding an average Resilience Gap of 7.57% and highlighting variability in attack effectiveness and evaluator reliability.
The paper introduces Trustworthy RAG, an evaluation agent designed to detect misinformation and knowledge poisoning in Retrieval-Augmented Generation systems. It combines natural language inference verification, a five-signal poison detector, and a weighted Trust Index to assess the reliability of retrieved content. Experiments on multiple LLMs show high accuracy and precision, with the agent effectively blocking unsafe advice in a secure-coding assistant scenario.
arXiv:2606. 02640v1 Announce Type: cross Abstract: Multi-turn jailbreak attacks pose a growing threat to large language model (LLM) safety because they exploit feedback from auxiliary judge models to iteratively refine prompts toward harmful goals.