Pith. sign in

REVIEW 5 cited by

Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstruction

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2402.18104 v2 pith:YMGYXB4I submitted 2024-02-28 cs.CR cs.AI

Tong Liu , Yingjie Zhang , Zhe Zhao , Yinpeng Dong , Guozhu Meng , Kai Chen This is my paper · ORCID
classification cs.CRcs.AI
keywords harmfulllmsattackdisguisemodelssuccessacrossjailbreak
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In recent years, large language models (LLMs) have demonstrated notable success across various tasks, but the trustworthiness of LLMs is still an open problem. One specific threat is the potential to generate toxic or harmful responses. Attackers can craft adversarial prompts that induce harmful responses from LLMs. In this work, we pioneer a theoretical foundation in LLMs security by identifying bias vulnerabilities within the safety fine-tuning and design a black-box jailbreak method named DRA (Disguise and Reconstruction Attack), which conceals harmful instructions through disguise and prompts the model to reconstruct the original harmful instruction within its completion. We evaluate DRA across various open-source and closed-source models, showcasing state-of-the-art jailbreak success rates and attack efficiency. Notably, DRA boasts a 91.1% attack success rate on OpenAI GPT-4 chatbot.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

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

  1. MOCHA: Are Code Language Models Robust Against Multi-Turn Malicious Coding Prompts?

    cs.CL 2025-07 conditional novelty 7.0 of 10

    MOCHA is a benchmark of 10.5K malicious coding prompts, including multi-turn decomposition attacks, showing code LLMs reject these incremental attacks at much lower rates and that fine-tuning on the benchmark improves...

  2. Behind the Mask: Benchmarking Camouflaged Jailbreaks in Large Language Models

    cs.CR 2025-09 conditional novelty 5.0 of 10

    A benchmark of 500 camouflaged jailbreak prompts finds open-weight LLMs comply with 94% of harmful requests, but the result is confounded by task complexity and an overly permissive compliance metric.

  3. 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.

  4. KDA: A Knowledge-Distilled Attacker for Generating Diverse Prompts to Jailbreak LLMs

    cs.CR 2025-02 conditional novelty 5.0 of 10

    A distilled open-source attacker, KDA, imitates three jailbreak methods to write diverse attack prompts, and reports higher success and efficiency than each teacher.

  5. SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation

    cs.CR 2025-06 conditional novelty 3.0 of 10

    A systematization-of-knowledge survey that categorizes LLM privacy risks into training data, prompts, outputs, and agents, and reviews limitations of current mitigations.

Pith tools