DecipherGuard: Understanding and Deciphering Jailbreak Prompts for a Safer Deployment of Intelligent Software Systems

• URL: http://arxiv.org/abs/2509.16870v1
• Date: Sun, 21 Sep 2025 01:46:35 GMT
• Title: DecipherGuard: Understanding and Deciphering Jailbreak Prompts for a Safer Deployment of Intelligent Software Systems
• Authors: Rui Yang, Michael Fu, Chakkrit Tantithamthavorn, Chetan Arora, Gunel Gulmammadova, Joey Chua,
• Abstract summary: DecipherGuard is a novel framework that integrates a deciphering layer to counter obfuscation-based prompts and a low-rank adaptation mechanism to enhance guardrail effectiveness against jailbreak attacks.<n> Empirical evaluation on over 22,000 prompts demonstrates that DecipherGuard improves DSR by 36% to 65% and Overall Guardrail Performance (OGP) by 20% to 50% compared to LlamaGuard and two other runtime guardrails.
• Score: 11.606665113249298
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intelligent software systems powered by Large Language Models (LLMs) are increasingly deployed in critical sectors, raising concerns about their safety during runtime. Through an industry-academic collaboration when deploying an LLM-powered virtual customer assistant, a critical software engineering challenge emerged: how to enhance a safer deployment of LLM-powered software systems at runtime? While LlamaGuard, the current state-of-the-art runtime guardrail, offers protection against unsafe inputs, our study reveals a Defense Success Rate (DSR) drop of 24% under obfuscation- and template-based jailbreak attacks. In this paper, we propose DecipherGuard, a novel framework that integrates a deciphering layer to counter obfuscation-based prompts and a low-rank adaptation mechanism to enhance guardrail effectiveness against template-based attacks. Empirical evaluation on over 22,000 prompts demonstrates that DecipherGuard improves DSR by 36% to 65% and Overall Guardrail Performance (OGP) by 20% to 50% compared to LlamaGuard and two other runtime guardrails. These results highlight the effectiveness of DecipherGuard in defending LLM-powered software systems against jailbreak attacks during runtime.

Related papers

• Bypassing Prompt Guards in Production with Controlled-Release Prompting [11.65770031195044]
  We introduce a new attack that circumvents prompt guards, highlighting their limitations.<n>Our method consistently jailbreaks production models while maintaining response quality.<n>This reveals an attack surface inherent to lightweight prompt guards in modern LLM architectures.
  arXiv  Detail & Related papers  (2025-10-02T00:04:21Z)
• AdaptiveGuard: Towards Adaptive Runtime Safety for LLM-Powered Software [11.606665113249298]
  Guardrails are critical for the safe deployment of Large Language Models (LLMs)-powered software.<n>We propose AdaptiveGuard, an adaptive guardrail that detects novel jailbreak attacks as out-of-distribution (OOD) inputs.<n>We show that AdaptiveGuard achieves 96% OOD detection accuracy, adapts to new attacks in just two update steps, and retains over 85% F1-score on in-distribution data post-adaptation.
  arXiv  Detail & Related papers  (2025-09-21T01:22:42Z)
• ARMOR: Aligning Secure and Safe Large Language Models via Meticulous Reasoning [64.32925552574115]
  ARMOR is a large language model that analyzes jailbreak strategies and extracts the core intent.<n> ARMOR achieves state-of-the-art safety performance, with an average harmful rate of 0.002 and an attack success rate of 0.06 against advanced optimization-based jailbreaks.
  arXiv  Detail & Related papers  (2025-07-14T09:05:54Z)
• SEALGuard: Safeguarding the Multilingual Conversations in Southeast Asian Languages for LLM Software Systems [9.469589800082597]
  This paper introduces SEALGuard, a multilingual guardrail designed to improve the safety alignment across diverse languages.<n>It aims to address the multilingual safety alignment gap of existing guardrails and ensure effective filtering of unsafe and jailbreak prompts.<n>We construct SEALSBench, a large-scale multilingual safety alignment dataset containing over 260,000 prompts in ten languages.
  arXiv  Detail & Related papers  (2025-07-11T05:15:35Z)
• LLMs Caught in the Crossfire: Malware Requests and Jailbreak Challenges [70.85114705489222]
  We propose MalwareBench, a benchmark dataset containing 3,520 jailbreaking prompts for malicious code-generation.<n>M MalwareBench is based on 320 manually crafted malicious code generation requirements, covering 11 jailbreak methods and 29 code functionality categories.<n>Experiments show that mainstream LLMs exhibit limited ability to reject malicious code-generation requirements, and the combination of multiple jailbreak methods further reduces the model's security capabilities.
  arXiv  Detail & Related papers  (2025-06-09T12:02:39Z)
• Why Not Act on What You Know? Unleashing Safety Potential of LLMs via Self-Aware Guard Enhancement [48.50995874445193]
  Large Language Models (LLMs) have shown impressive capabilities across various tasks but remain vulnerable to meticulously crafted jailbreak attacks.<n>We propose SAGE (Self-Aware Guard Enhancement), a training-free defense strategy designed to align LLMs' strong safety discrimination performance with their relatively weaker safety generation ability.
  arXiv  Detail & Related papers  (2025-05-17T15:54:52Z)
• Bypassing LLM Guardrails: An Empirical Analysis of Evasion Attacks against Prompt Injection and Jailbreak Detection Systems [4.225223514207515]
  Large Language Models (LLMs) guardrail systems are designed to protect against prompt injection and jailbreak attacks.<n>We demonstrate two approaches for bypassing prompt injection and jailbreak detection systems.<n>We show that both methods can be used to evade detection while maintaining adversarial utility.
  arXiv  Detail & Related papers  (2025-04-15T13:16:02Z)
• CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment [66.72332011814183]
  CoreGuard is a computation- and communication-efficient protection method for proprietary large language models (LLMs) deployed on edge devices.<n> CoreGuard employs an efficient protection protocol to reduce computational overhead and minimize communication overhead via a propagation protocol.
  arXiv  Detail & Related papers  (2024-10-16T08:14:24Z)
• MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard for Prompt Attacks [2.873719680183099]
  This paper advocates for the significance of jailbreak attack prevention on Large Language Models (LLMs) We introduce MoJE, a novel guardrail architecture designed to surpass current limitations in existing state-of-the-art guardrails. MoJE excels in detecting jailbreak attacks while maintaining minimal computational overhead during model inference.
  arXiv  Detail & Related papers  (2024-09-26T10:12:19Z)
• Defensive Prompt Patch: A Robust and Interpretable Defense of LLMs against Jailbreak Attacks [59.46556573924901]
  This paper introduces Defensive Prompt Patch (DPP), a novel prompt-based defense mechanism for large language models (LLMs)<n>Unlike previous approaches, DPP is designed to achieve a minimal Attack Success Rate (ASR) while preserving the high utility of LLMs.<n> Empirical results conducted on LLAMA-2-7B-Chat and Mistral-7B-Instruct-v0.2 models demonstrate the robustness and adaptability of DPP.
  arXiv  Detail & Related papers  (2024-05-30T14:40:35Z)
• Jailbreaking Black Box Large Language Models in Twenty Queries [97.29563503097995]
  Large language models (LLMs) are vulnerable to adversarial jailbreaks. We propose an algorithm that generates semantic jailbreaks with only black-box access to an LLM.
  arXiv  Detail & Related papers  (2023-10-12T15:38:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.