A red teaming framework for large language models: a case study on faithfulness evaluation

Explaining Jailbreaks: Structured and Interpretable Safety Assessment for Large Language Models

This work proposes an explanation-aware safety framework that augments binary harmfulness detection with structured, human-interpretable explanations capturing severity, strategies, trigger spans, ratio-nales, and derived safety factors, and introduces a human–LLM hybrid annotation and canonicaliza-tion pipeline.

No One Model Catches Every Harm: Benchmarking Content Moderation Across Safety Scenarios

This work presents the most comprehensive evaluation of LLM safety capabilities to date, systematically testing models across datasets that are organized into four distinct categories, and uncovers critical blind spots.

Do LLMs Know Their Vulnerable Scenarios?

Safety-aligned large language models are trained to refuse harmful requests, yet embedding the same requests in particular scenarios can bypass their safeguards. Existing red-teaming methods empirically identify effective scenarios through observed attack outcomes, but why particular scenarios weaken refusal remains mechanistically unclear. Meanwhile, mechanistic interpretability studies have characterized both refusal directions and jailbreak-associated features, without explaining the relationship between the two representations. In this work, we show that scenario-wrapped prompts activate internal scenario directions whose causal steering consistently reduces refusal scores. Building on this finding, we propose \textsc{Concept2Scenario}, a concept-based attribution framework for vulnerable scenario discovery. It instantiates a broad concept space with a sparse autoencoder, attributes refusal suppression to individual concepts, translates the identified concepts into interpretable natural-language scenarios, and identifies synergistic scenario combinations through interaction attribution. Across three open-source models, two safety benchmarks, and six black-box jailbreak methods, the discovered scenarios serve as reusable priors that improve average attack success rates by up to $18.2$ percentage points. They also transfer to GPT-5, Claude-Haiku-4.5, and Gemini-3-Flash, suggesting that some scenario-level refusal vulnerabilities are shared across model families. Moreover, the identified combinations outperform their individual constituents and enable iterative attacks to succeed in fewer turns.

You Are an Expert: RAG Injection and Guided Error Expert Activation for Jailbreaking Large Language Models

With the rapid development and widespread deployment of large language models (LLMs), the security and robustness of these models have emerged as critical research topics. Among various threats, jailbreak attacks, which aim to circumvent built‐in safety mechanisms, have garnered considerable attention as a key means of breaching model protections. However, existing jailbreak methods still face several limitations, such as excessive reliance on the model's internal capabilities, high attack costs and insufficiently comprehensive evaluation strategies. To address these challenges, we propose a novel jailbreak framework: YAE. This method enhances the model's dependency on external information by constructing a RAG‐based knowledge base containing harmful Q&A content. It also leverages Guide MoE, a routing‐inspired prompting strategy, to guide the model toward alternative reasoning trajectories through expert‐role prompting and domain‐specific perturbations, thereby inducing more deceptive responses. Furthermore, Adversarial Fine‐Tuning is employed to further optimize the performance of jailbreak attacks. We conduct extensive experiments under various settings, and the results demonstrate that YAE achieves state‐of‐the‐art performance in terms of both attack success rate (ASR) and efficiency, highlighting its strong overall effectiveness.

Large Language Model Vulnerabilities

Dual-Adversarial Safety Alignment: Cultivating Intrinsic Threat Comprehension in LRMs

Experiments show that with only 1K synthesized samples, AdvSafe-aligned LRMs achieve significantly stronger jailbreak robustness than existing baselines, with almost no utility degradation, demonstrating that learning unsafety knowledge enables a superior robustness-utility trade-off and generalizes beyond seen attack patterns.