SlotGCG uses Vulnerable Slot Score (VSS) to identify and target the most vulnerable prompt positions for adversarial token insertion, delivering 14% higher ASR than standard GCG and 42% higher against defenses.
Weak-to-strong jailbreaking on large language models
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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background 2representative citing papers
Contrastive Logit Steering isolates a linear refusal direction in safety-aligned LLMs, achieving higher jailbreak success than activation steering and enabling bidirectional control without retraining.
CausalDetox identifies minimal attention heads causally linked to toxicity via Probability of Necessity and Sufficiency, then applies targeted inference-time steering or fine-tuning to reduce toxic generation while preserving fluency and achieving faster selection.
Formalizes the jailbreak oracle problem for LLMs and introduces Boa, a two-phase breadth-first then depth-first search system to solve it efficiently.
AP-Test identifies deployed guardrails in LLMs via adversarial prompt testing and a match score metric, reporting perfect accuracy on four open-source guardrails.
Proposes a trust schema including verification levels and a biconditional correctness criterion to verify skills in human-in-the-loop agent runtimes, reducing the need for constant oversight.
A survey that creates taxonomies for jailbreak attacks and defenses on LLMs, subdivides them into sub-classes, and compares evaluation approaches.
A comprehensive survey that taxonomizes safety threats to large models and agents, reviews defenses and benchmarks, and outlines open challenges.
citing papers explorer
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SlotGCG: Exploiting the Positional Vulnerability in LLMs for Jailbreak Attacks
SlotGCG uses Vulnerable Slot Score (VSS) to identify and target the most vulnerable prompt positions for adversarial token insertion, delivering 14% higher ASR than standard GCG and 42% higher against defenses.
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The Geometry of Refusal: Linear Instability in Safety-Aligned LLMs
Contrastive Logit Steering isolates a linear refusal direction in safety-aligned LLMs, achieving higher jailbreak success than activation steering and enabling bidirectional control without retraining.
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CausalDetox: Causal Head Selection and Intervention for Language Model Detoxification
CausalDetox identifies minimal attention heads causally linked to toxicity via Probability of Necessity and Sufficiency, then applies targeted inference-time steering or fine-tuning to reduce toxic generation while preserving fluency and achieving faster selection.
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Toward Principled LLM Safety Testing: Solving the Jailbreak Oracle Problem
Formalizes the jailbreak oracle problem for LLMs and introduces Boa, a two-phase breadth-first then depth-first search system to solve it efficiently.
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Peering Behind the Shield: Guardrail Identification in Large Language Models
AP-Test identifies deployed guardrails in LLMs via adversarial prompt testing and a match score metric, reporting perfect accuracy on four open-source guardrails.
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Skills as Verifiable Artifacts: A Trust Schema and a Biconditional Correctness Criterion for Human-in-the-Loop Agent Runtimes
Proposes a trust schema including verification levels and a biconditional correctness criterion to verify skills in human-in-the-loop agent runtimes, reducing the need for constant oversight.
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Jailbreak Attacks and Defenses Against Large Language Models: A Survey
A survey that creates taxonomies for jailbreak attacks and defenses on LLMs, subdivides them into sub-classes, and compares evaluation approaches.
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Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
A comprehensive survey that taxonomizes safety threats to large models and agents, reviews defenses and benchmarks, and outlines open challenges.