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Against The Achilles' Heel: A Survey on Red Teaming for Generative Models
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Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safe use as various vulnerabilities are exposed. In light of this, the field of red teaming is undergoing fast-paced growth, highlighting the need for a comprehensive survey covering the entire pipeline and addressing emerging topics. Our extensive survey, which examines over 120 papers, introduces a taxonomy of fine-grained attack strategies grounded in the inherent capabilities of language models. Additionally, we have developed the "searcher" framework to unify various automatic red teaming approaches. Moreover, our survey covers novel areas including multimodal attacks and defenses, risks around LLM-based agents, overkill of harmless queries, and the balance between harmlessness and helpfulness.
Forward citations
Cited by 3 Pith papers
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Adaptively Robust LLM Monitoring via Activation Watermarking
Activation Watermarking embeds a secret keyed direction in an LLM's hidden states so policy-violating responses can be detected by a cosine test, cutting adaptive-jailbreak evasion relative to guard models.
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Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation
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.
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Prompt Optimization and Evaluation for LLM Automated Red Teaming
Applying Attack Success Rate to individual attacks via repeated testing yields an ASR distribution that, when used to mine contrastive pairs, improves automated red-teaming prompt optimization over single-try ASR.
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