BitHydra: Towards Bit-flip Inference Cost Attack against Large Language Models (2505.16670v2)

Abstract: LLMs have shown impressive capabilities across a wide range of applications, but their ever-increasing size and resource demands make them vulnerable to inference cost attacks, where attackers induce victim LLMs to generate the longest possible output content. In this paper, we revisit existing inference cost attacks and reveal that these methods can hardly produce large-scale malicious effects since they are self-targeting, where attackers are also the users and therefore have to execute attacks solely through the inputs, whose generated content will be charged by LLMs and can only directly influence themselves. Motivated by these findings, this paper introduces a new type of inference cost attacks (dubbed 'bit-flip inference cost attack') that target the victim model itself rather than its inputs. Specifically, we design a simple yet effective method (dubbed 'BitHydra') to effectively flip critical bits of model parameters. This process is guided by a loss function designed to suppress <EOS> token's probability with an efficient critical bit search algorithm, thus explicitly defining the attack objective and enabling effective optimization. We evaluate our method on 11 LLMs ranging from 1.5B to 14B parameters under both int8 and float16 settings. Experimental results demonstrate that with just 4 search samples and as few as 3 bit flips, BitHydra can force 100% of test prompts to reach the maximum generation length (e.g., 2048 tokens) on representative LLMs such as LLaMA3, highlighting its efficiency, scalability, and strong transferability across unseen inputs.

BitHydra: Bit-flip Inference Cost Attack on LLMs

In "BitHydra: Towards Bit-flip Inference Cost Attack against LLMs," the authors address vulnerabilities in LLMs concerning inference cost attacks. The paper scrutinizes existing methods and introduces a novel approach leveraging bit-flip attacks, marking a departure from traditional input-manipulation strategies.

Key Contributions

1. Identification of LLM Vulnerabilities: The paper emphasizes the inherent limitations of conventional inference cost attacks, which focus on crafting input prompts to elongate output generation. These are termed "self-targeting," as attackers themselves are indirectly affected by incurring costs without substantial external impact.
2. BitHydra Framework: The introduction of BitHydra presents a paradigm shift by targeting model parameters instead of inputs. By flipping critical bits within model weights, the method seeks to universally inflate inference costs, affecting all user interactions with a compromised model.
3. Optimization of Bit-Flips: A sophisticated search algorithm identifies crucial bits to disrupt the generation of <EOS> tokens, thereby maximizing output length. BitHydra requires as few as 3 flipped bits, demonstrating efficiency by reaching the maximum token generation (2048 tokens) with significant consistency.
4. Extensive Evaluation: Eleven LLMs ranging from 1.5B to 14B parameters were evaluated, demonstrating BitHydra's scalability and robust transferability across diverse models and settings (int8 and float16). The technique achieved a 100% success rate in ensuring the maximum output length on several models, underscoring its effectiveness.

Critical Insights and Findings

• Efficiency: BitHydra efficiently identifies impactful bit positions with a minimal number of samples, thereby reducing computational overhead. A streamlined approach is critical for practical application, especially when targeting models hosted on cloud-based platforms.
• Model Quality Preservation: Despite manipulating model weights, the attack preserves the syntactic and semantic validity of outputs. This stealth characteristic is crucial for evading detection while maximizing disruption.
• Resistance to Defenses: Common defenses such as fine-tuning and weight reconstruction present limited efficacy against the attack strategy, highlighting the need for more robust countermeasures in AI systems.

Implications and Future Directions

This work raises critical considerations for both theoretical exploration and practical safeguarding in AI systems. By shifting the attack focus to element-wise vulnerabilities in model weights, BitHydra illuminates pathways to enhanced attack strategies that could exploit hardware-based weaknesses. Such insights call for dedicated attention to the security protocols surrounding AI deployments, particularly concerning model integrity and operational cost management.

The presented attack strategy invites a broader discourse regarding the resilience of LLMs against parameter manipulation, which could inform future research into detection mechanisms and smarter defensive architectures. Moreover, an exploration into the adaptive measures for mitigating such risks, possibly leveraging innovative software and hardware-based solutions, is a compelling direction.

In sum, BitHydra invites the research community to redouble efforts towards fortifying LLM infrastructures against targeted inference cost attacks. The paper serves as a foundational step to understand and address vulnerabilities in modern AI systems, aligning with the ongoing pursuits in AI security and safeguarding technology trust.