TFL: Targeted Bit-Flip Attack on Large Language Model
arXiv:2602.17837v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in safety and security critical applications, raising concerns about their robustness to model parameter fault injection attacks. Recent studies have shown that bit-flip attacks (BFAs), which exploit computer main memory (i.e., DRAM) vulnerabilities to flip a small number of bits in model weights, can severely disrupt LLM behavior. However, existing BFA on LLM largely induce un-targeted failure or general performance degradation, offering limited control over manipulating specific or targeted outputs. In this paper, we present TFL, a novel targeted bit-flip attack framework that enables precise manipulation of LLM outputs for selected prompts while maintaining almost no or minor degradation on unrelated inputs. Within our TFL framework, we propose a novel keyword-focused attack loss to promote attacker-specified target tokens in generative outputs, together with an auxiliary utilit
arXiv:2602.17837v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in safety and security critical applications, raising concerns about their robustness to model parameter fault injection attacks. Recent studies have shown that bit-flip attacks (BFAs), which exploit computer main memory (i.e., DRAM) vulnerabilities to flip a small number of bits in model weights, can severely disrupt LLM behavior. However, existing BFA on LLM largely induce un-targeted failure or general performance degradation, offering limited control over manipulating specific or targeted outputs. In this paper, we present TFL, a novel targeted bit-flip attack framework that enables precise manipulation of LLM outputs for selected prompts while maintaining almost no or minor degradation on unrelated inputs. Within our TFL framework, we propose a novel keyword-focused attack loss to promote attacker-specified target tokens in generative outputs, together with an auxiliary utility score that balances attack effectiveness against collateral performance impact on benign data. We evaluate TFL on multiple LLMs (Qwen, DeepSeek, Llama) and benchmarks (DROP, GSM8K, and TriviaQA). The experiments show that TFL achieves successful targeted LLM output manipulations with less than 50 bit flips and significantly reduced effect on unrelated queries compared to prior BFA approaches. This demonstrates the effectiveness of TFL and positions it as a new class of stealthy and targeted LLM model attack.
Executive Summary
This article presents a novel targeted bit-flip attack framework, TFL, designed to manipulate large language model (LLM) outputs for specific prompts while minimizing impact on unrelated inputs. TFL achieves this through a keyword-focused attack loss and auxiliary utility score, outperforming prior bit-flip attack (BFA) approaches. The authors evaluate TFL on multiple LLMs and benchmarks, demonstrating successful targeted output manipulations with less than 50 bit flips and reduced collateral damage. This breakthrough positions TFL as a stealthy and targeted LLM model attack, raising concerns about the robustness of LLMs in safety and security-critical applications.
Key Points
- ▸ TFL is a novel targeted bit-flip attack framework for large language models (LLMs)
- ▸ TFL achieves precise manipulation of LLM outputs for selected prompts with minimal collateral damage
- ▸ TFL outperforms prior BFA approaches in terms of attack effectiveness and reduced impact on unrelated queries
Merits
Strength
TFL's ability to target specific LLM outputs with minimal collateral damage is a significant improvement over prior BFA approaches
Methodological Contribution
The authors' use of a keyword-focused attack loss and auxiliary utility score represents a novel approach to targeted LLM attacks
Demerits
Limitation
The authors do not provide a comprehensive analysis of the potential risks and consequences of TFL in real-world applications
Scalability
The authors' evaluation of TFL on multiple LLMs and benchmarks may not be scalable to larger and more complex models
Expert Commentary
The authors' presentation of TFL is a significant contribution to the field of adversarial attacks on LLMs. However, the article would benefit from a more comprehensive analysis of the potential risks and consequences of TFL in real-world applications. Furthermore, the authors should consider evaluating TFL on more complex models to assess its scalability. Nonetheless, TFL is a novel and effective targeted LLM attack that highlights the need for more robust and secure models.
Recommendations
- ✓ Recommendation 1: Future research should focus on developing more robust and secure LLMs that can withstand targeted attacks like TFL
- ✓ Recommendation 2: The development of TFL highlights the need for more stringent testing and evaluation of LLMs in safety and security-critical applications
