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[2505.16670] BitHydra: Towards Bit-flip Inference Cost Attack against Large Language Models
Summary
The paper presents BitHydra, a framework for executing bit-flip inference cost attacks on large language models (LLMs), demonstrating how minor parameter alterations can significantly increase output length.
Why It Matters
As large language models become increasingly prevalent, understanding their vulnerabilities is crucial for ensuring AI safety. BitHydra highlights a novel attack method that could lead to more efficient exploitation of LLMs, raising concerns about their security and reliability in real-world applications.
Key Takeaways
- BitHydra targets LLMs by manipulating model parameters rather than inputs.
- The attack maximizes inference costs through minimal weight perturbations.
- The framework uses Binary Integer Programming to optimize the attack process.
- Results show effective endless generation with only 1-4 bit flips across various models.
- The findings underscore the need for improved defenses against such vulnerabilities.
Computer Science > Cryptography and Security arXiv:2505.16670 (cs) [Submitted on 22 May 2025 (v1), last revised 21 Feb 2026 (this version, v4)] Title:BitHydra: Towards Bit-flip Inference Cost Attack against Large Language Models Authors:Xiaobei Yan, Yiming Li, Hao Wang, Han Qiu, Tianwei Zhang View a PDF of the paper titled BitHydra: Towards Bit-flip Inference Cost Attack against Large Language Models, by Xiaobei Yan and 4 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) are widely deployed, but their substantial compute demands make them vulnerable to inference cost attacks that aim to deliberately maximize the output length. In this work, we investigate a distinct attack surface: maximizing inference cost by tampering with the model parameters instead of inputs. This approach leverages the established capability of Bit-Flip Attacks (BFAs) to persistently alter model behavior via minute weight perturbations, effectively decoupling the attack from specific input queries. To realize this, we propose BitHydra, a framework that addresses the unique optimization challenge of identifying the exact weight bits that maximize generation cost. We formulate the attack as a constrained Binary Integer Programming (BIP) problem designed to systematically suppress the end-of-sequence (i.e., <eos>) probability. To overcome the intractability of the discrete search space, we relax the problem into a continuous optimization task and solve it via the Alternati...
