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LMVD-ID: 1f0fcf78
Paper published February 1, 2025
Entry analyzed March 19, 2025
Paper-reported evidence
Confidence: Source-linked

The LMVD-ID is an internal research identifier, not an official CVE identifier.

CRI Jailbreak Initialization

CRI (Compliance Refusal Initialization) initializes jailbreak attacks by leveraging pre-trained jailbreak prompts, effectively guiding the optimization process towards the compliance subspace of harmful prompts. This…

BibTeX citation

Paper-evaluated models(8)

Falcon 7B Instruct, Llama 2 7B Chat, Llama 3 8B Instruct +5 more
  • Falcon 7B Instruct
  • Llama 2 7B Chat
  • Llama 3 8B Instruct
  • Mistral 7B Instruct v0.2
  • Mistral 7B Instruct v0.3
  • Phi-4
  • Qwen 2.5 Coder 7B Instruct
  • Vicuna 7B v1.3

Description

CRI (Compliance Refusal Initialization) initializes jailbreak attacks by leveraging pre-trained jailbreak prompts, effectively guiding the optimization process towards the compliance subspace of harmful prompts. This significantly enhances the success rate and reduces the computational overhead of attacks, often requiring only a single optimization step to bypass safety mechanisms. Attacks utilizing CRI demonstrate significantly improved ASR (Adversarial Success Rate) and reduced median steps to success.

Examples

See arXiv:2502.09755 (opens in a new tab). Specifically, observe the results in Figures 3, 4, and 5, which demonstrate the increased ASR of attacks (GCG, AutoDAN-GA, and AutoDAN-HGA respectively) across various LLMs when using CRI initialization compared to standard or random initialization. Real-world examples of jailbreak prompts and model responses are provided in Appendix D and E of the linked paper. For instance comparing standard initialization with CRI initialization in Figure 2, with the latter achieving a jailbreak in two optimization steps.

Impact

LLMs that are vulnerable to CRI-based jailbreak attacks can be coerced into generating harmful, unethical, or otherwise undesirable content, potentially leading to the spread of misinformation, malicious code generation, or the facilitation of illegal activities. Existing safety mechanisms are bypassed or rendered ineffective.

Affected Systems

Large Language Models (LLMs) susceptible to gradient-based jailbreak attacks, including but not limited to Llama-2, Vicuna, and Llama-3.

Mitigation Steps

  • Implement robust refusal mechanisms: Develop and enhance the LLM's ability to identify and refuse harmful prompts.
  • Adversarial Training: Integrate pre-trained jailbreak transformations into adversarial training to improve model resilience.
  • Monitor and Detect Jailbreak Attempts: Implement monitoring systems to detect and flag unusual or malicious prompts that exploit vulnerabilities like those described in the paper cited.

Research context and confidence

Evidence and verification
Paper-reported; independent reproduction is not documented.
Primary research source linked.
Severity
Not rated by this catalog.
Source and publication type
arXiv · Research preprint.
Peer-review status is not provided by this source.
Author and publication status
Author metadata is not stored; see the primary paper.
Threat model and attacker access
Both white-box and black-box research contexts are tagged; consult the primary paper for target-specific access.
Related deployment categories
No related deployment category is classified.
Taxonomy labels only; paper-specific deployment prerequisites are not inferred.
Affected systems
Large Language Models (LLMs) susceptible to gradient-based jailbreak attacks, including but not limited to Llama-2, Vicuna, and Llama-3.

Research Paper

Jailbreak Attack Initializations as Extractors of Compliance Directions

Primary source: arXiv. Findings are reported by the cited research and have not been independently verified.

View Paper

Evidence

This entry is based on a primary research source. Its findings are paper-reported; independent reproduction and verification are not claimed.

https://arxiv.org/abs/2502.09755