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Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey

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arxiv 2402.09283 v3 pith:FERCOTQI submitted 2024-02-14 cs.CL cs.AIcs.CYcs.LG

Zhichen Dong , Zhanhui Zhou , Chao Yang , Jing Shao , Yu Qiao This is my paper
classification cs.CLcs.AIcs.CYcs.LG
keywords conversationsafetysurveyattacksdefensesevaluationsrecentstudies
verification ladder T0 review T1 audit T2 compute T3 formal
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Large Language Models (LLMs) are now commonplace in conversation applications. However, their risks of misuse for generating harmful responses have raised serious societal concerns and spurred recent research on LLM conversation safety. Therefore, in this survey, we provide a comprehensive overview of recent studies, covering three critical aspects of LLM conversation safety: attacks, defenses, and evaluations. Our goal is to provide a structured summary that enhances understanding of LLM conversation safety and encourages further investigation into this important subject. For easy reference, we have categorized all the studies mentioned in this survey according to our taxonomy, available at: https://github.com/niconi19/LLM-conversation-safety.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. YouthSafe: A Youth-Centric Safety Benchmark and Safeguard Model for Large Language Models

    cs.HC 2025-09 conditional novelty 6.0 of 10

    Introduces YAIR, a youth-GenAI risk benchmark, and YouthSafe, a fine-tuned classifier with AUPRC 0.94 on it, though it compares a trained model to untrained baselines.

  2. SafeWork-R1: Coevolving Safety and Intelligence under the AI-45$^{\circ}$ Law

    cs.AI 2025-07 conditional novelty 6.0 of 10

    SafeWork-R1 shows that a staged RL pipeline with safety, value, and knowledge verifiers can improve both safety and general reasoning scores over a base multimodal model.

  3. MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security

    cs.CL 2025-09 conditional novelty 5.0 of 10

    MoGUv2 embeds small routers in the deeper layers of LLMs to dynamically blend a helpful variant and a refusal variant, improving safety against jailbreak and fine-tuning attacks while preserving usability.

  4. Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation

    cs.CL 2025-08 reject novelty 5.0 of 10

    Sparse autoencoder activation perturbation (SFPF) applied on top of existing jailbreak prompts raises attack success rate on Qwen3-32B, but with no defense evaluation and weak reproducibility.

  5. Should LLM Safety Be More Than Refusing Harmful Instructions?

    cs.CL 2025-06 conditional novelty 5.0 of 10

    LLMs that can decrypt common ciphers show safety failures split across two dimensions, refusing too much or generating unsafe output, and current defenses fix one side while breaking the other.

  6. An Empirical Study of Vulnerable Package Dependencies in LLM Repositories

    cs.CR 2025-08 conditional novelty 4.0 of 10

    In 52 open-source LLM projects, 75.8% of those with dependency configs use at least one vulnerable package, and half of supply chain vulnerabilities stay undisclosed for over 56 months.

  7. The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs

    cs.CL 2025-06 conditional novelty 3.0 of 10

    A structured survey of LLM safety evaluation that proposes a why/what/where/how taxonomy and catalogs metrics, datasets, benchmarks, evaluators, and frameworks.

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