Secure Tug-of-War (SecTOW): Iterative Defense-Attack Training with Reinforcement Learning for Multimodal Model Security

• URL: http://arxiv.org/abs/2507.22037v1
• Date: Tue, 29 Jul 2025 17:39:48 GMT
• Title: Secure Tug-of-War (SecTOW): Iterative Defense-Attack Training with Reinforcement Learning for Multimodal Model Security
• Authors: Muzhi Dai, Shixuan Liu, Zhiyuan Zhao, Junyu Gao, Hao Sun, Xuelong Li,
• Abstract summary: We propose Secure Tug-of-War (SecTOW) to enhance the security of multimodal large language models (MLLMs)<n>SecTOW consists of two modules: a defender and an auxiliary attacker, both trained iteratively using reinforcement learning (GRPO)<n>We show that SecTOW significantly improves security while preserving general performance.
• Score: 63.41350337821108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of multimodal large language models (MLLMs) has led to breakthroughs in various applications, yet their security remains a critical challenge. One pressing issue involves unsafe image-query pairs--jailbreak inputs specifically designed to bypass security constraints and elicit unintended responses from MLLMs. Compared to general multimodal data, such unsafe inputs are relatively sparse, which limits the diversity and richness of training samples available for developing robust defense models. Meanwhile, existing guardrail-type methods rely on external modules to enforce security constraints but fail to address intrinsic vulnerabilities within MLLMs. Traditional supervised fine-tuning (SFT), on the other hand, often over-refuses harmless inputs, compromising general performance. Given these challenges, we propose Secure Tug-of-War (SecTOW), an innovative iterative defense-attack training method to enhance the security of MLLMs. SecTOW consists of two modules: a defender and an auxiliary attacker, both trained iteratively using reinforcement learning (GRPO). During the iterative process, the attacker identifies security vulnerabilities in the defense model and expands jailbreak data. The expanded data are then used to train the defender, enabling it to address identified security vulnerabilities. We also design reward mechanisms used for GRPO to simplify the use of response labels, reducing dependence on complex generative labels and enabling the efficient use of synthetic data. Additionally, a quality monitoring mechanism is used to mitigate the defender's over-refusal of harmless inputs and ensure the diversity of the jailbreak data generated by the attacker. Experimental results on safety-specific and general benchmarks demonstrate that SecTOW significantly improves security while preserving general performance.

Related papers

• SafePTR: Token-Level Jailbreak Defense in Multimodal LLMs via Prune-then-Restore Mechanism [123.54980913741828]
  Multimodal Large Language Models (MLLMs) extend LLMs to support visual reasoning.<n>MLLMs are susceptible to multimodal jailbreak attacks and hindering their safe deployment.<n>We propose Safe Prune-then-Restore (SafePTR), a training-free defense framework that selectively prunes harmful tokens at vulnerable layers while restoring benign features at subsequent layers.
  arXiv  Detail & Related papers  (2025-07-02T09:22:03Z)
• Align is not Enough: Multimodal Universal Jailbreak Attack against Multimodal Large Language Models [83.80177564873094]
  We propose a unified multimodal universal jailbreak attack framework.<n>We evaluate the undesirable context generation of MLLMs like LLaVA, Yi-VL, MiniGPT4, MiniGPT-v2, and InstructBLIP.<n>This study underscores the urgent need for robust safety measures in MLLMs.
  arXiv  Detail & Related papers  (2025-06-02T04:33:56Z)
• One Trigger Token Is Enough: A Defense Strategy for Balancing Safety and Usability in Large Language Models [20.42976162135529]
  Large Language Models (LLMs) have been extensively used across diverse domains, including virtual assistants, automated code generation, and scientific research.<n>We propose textttD-STT, a simple yet effective defense algorithm that identifies and explicitly decodes safety trigger tokens of the given safety-aligned LLM.
  arXiv  Detail & Related papers  (2025-05-12T01:26:50Z)
• Tit-for-Tat: Safeguarding Large Vision-Language Models Against Jailbreak Attacks via Adversarial Defense [90.71884758066042]
  Large vision-language models (LVLMs) introduce a unique vulnerability: susceptibility to malicious attacks via visual inputs.<n>We propose ESIII (Embedding Security Instructions Into Images), a novel methodology for transforming the visual space from a source of vulnerability into an active defense mechanism.
  arXiv  Detail & Related papers  (2025-03-14T17:39:45Z)
• Enhancing Model Defense Against Jailbreaks with Proactive Safety Reasoning [21.423429565221383]
  Large language models (LLMs) are vital for a wide range of applications yet remain susceptible to jailbreak threats.<n>We propose a novel defense strategy, Safety Chain-of-Thought (SCoT), which harnesses the enhanced textitreasoning capabilities of LLMs for proactive assessment of harmful inputs.
  arXiv  Detail & Related papers  (2025-01-31T14:45:23Z)
• Latent-space adversarial training with post-aware calibration for defending large language models against jailbreak attacks [23.793583584784685]
  Large language models (LLMs) are susceptible to jailbreak attacks, which exploit system vulnerabilities to circumvent safety measures and elicit harmful or inappropriate outputs.<n>We introduce LATPC, a Latent-space Adrial Training with Post-aware framework.<n>LATPC identifies safety-critical latent dimensions by contrasting harmful and benign inputs, enabling the adaptive construction of targeted refusal feature removal attacks.
  arXiv  Detail & Related papers  (2025-01-18T02:57:12Z)
• 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)
• SafeAligner: Safety Alignment against Jailbreak Attacks via Response Disparity Guidance [48.36220909956064]
  SafeAligner is a methodology implemented at the decoding stage to fortify defenses against jailbreak attacks.<n>We develop two specialized models: the Sentinel Model, which is trained to foster safety, and the Intruder Model, designed to generate riskier responses.<n>We show that SafeAligner can increase the likelihood of beneficial tokens, while reducing the occurrence of harmful ones.
  arXiv  Detail & Related papers  (2024-06-26T07:15:44Z)
• 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)

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.