mlsanityData to 7 Oct 2026

Round Trip Translation Defence against Large Language Model Jailbreaking Attacks

arXiv 2402.13517 ↗First posted 21 Feb 2024cs.CL cs.AIcited by 6

On the map: LLM safety and security → Jailbreaking

Abstract (arXiv)

Large language models (LLMs) are susceptible to social-engineered attacks that are human-interpretable but require a high level of comprehension for LLMs to counteract. Existing defensive measures can only mitigate less than half of these attacks at most. To address this issue, we propose the Round Trip Translation (RTT) method, the first algorithm specifically designed to defend against social-engineered attacks on LLMs. RTT paraphrases the adversarial prompt and generalizes the idea conveyed, making it easier for LLMs to detect induced harmful behavior. This method is versatile, lightweight, and transferrable to different LLMs. Our defense successfully mitigated over 70% of Prompt Automatic Iterative Refinement (PAIR) attacks, which is currently the most effective defense to the best of our knowledge. We are also the first to attempt mitigating the MathsAttack and reduced its attack success rate by almost 40%. Our code is publicly available at https://github.com/Cancanxxx/Round_Trip_Translation_Defence This version of the article has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.48550/arXiv.2402.13517 Use of this Accepted Version is subject to the publisher's Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in Jailbreaking, one of 12 directions in LLM safety and security. Builds on 6 of its 10 references that are in the map. First posted on arXiv 21 Feb 2024.

Builds on

6 of 10 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Jailbreaking Black Box Large Language Models in Twenty Queries12 Oct
  2. 2023SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks5 Oct

Other references · 4

  1. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  2. 2018Interpretable Adversarial Perturbation in Input Embedding Space for Textcited by 67
  3. 2023MathAttack: Attacking Large Language Models Towards Math Solving Abilitycited by 31
  4. 2019Interpretable Adversarial Training for Textcited by 6

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 3.

6 papers.

  1. 2024Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs)cited by 44
  2. 2025Single-pass Detection of Jailbreaking Input in Large Language Modelscited by 10
  3. 2024Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerationscited by 8
  4. 2024AdvBDGen: Adversarially Fortified Prompt-Specific Fuzzy Backdoor Generator Against LLM Alignmentcited by 5
  5. 2025Geometry-Guided Adversarial Prompt Detection via Curvature and Local Intrinsic Dimensioncited by 1
  6. 2025Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMscited by 1