BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks and Defenses on Large Language Models (2408.12798v2)

Abstract: Generative LLMs have achieved state-of-the-art results on a wide range of tasks, yet they remain susceptible to backdoor attacks: carefully crafted triggers in the input can manipulate the model to produce adversary-specified outputs. While prior research has predominantly focused on backdoor risks in vision and classification settings, the vulnerability of LLMs in open-ended text generation remains underexplored. To fill this gap, we introduce BackdoorLLM (Our BackdoorLLM benchmark was awarded First Prize in the SafetyBench competition, https://www.mlsafety.org/safebench/winners, organized by the Center for AI Safety, https://safe.ai/.), the first comprehensive benchmark for systematically evaluating backdoor threats in text-generation LLMs. BackdoorLLM provides: (i) a unified repository of benchmarks with a standardized training and evaluation pipeline; (ii) a diverse suite of attack modalities, including data poisoning, weight poisoning, hidden-state manipulation, and chain-of-thought hijacking; (iii) over 200 experiments spanning 8 distinct attack strategies, 7 real-world scenarios, and 6 model architectures; (iv) key insights into the factors that govern backdoor effectiveness and failure modes in LLMs; and (v) a defense toolkit encompassing 7 representative mitigation techniques. Our code and datasets are available at https://github.com/bboylyg/BackdoorLLM. We will continuously incorporate emerging attack and defense methodologies to support the research in advancing the safety and reliability of LLMs.

• The paper demonstrates that backdoor attacks can significantly increase the attack success rate across various LLM architectures using strategies like data and weight poisoning.
• It systematically compares attack methods, revealing that larger models show resilience against weight poisoning while remaining vulnerable to chain-of-thought and hidden state manipulations.
• The benchmark, validated through over 200 experiments, provides actionable insights to advance robust defense strategies for securing generative AI systems.

Overview of BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks on LLMs

The paper "BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks on LLMs" presents a novel benchmark framework designed to rigorously evaluate backdoor vulnerabilities in generative LLMs. While previous research has largely concentrated on backdoor attacks within vision and text classification domains, this paper pioneers a systematic analysis of backdoor attacks in text generation, a significantly underexplored area.

The authors provide a robust repository, BackdoorLLM, equipped with standardized training pipelines for simulating backdoor scenarios in different model architectures and under various attack strategies. The benchmark encompasses an array of attack strategies, notably including data poisoning, weight poisoning, hidden state manipulation, and chain-of-thought attacks. Crucially, the paper evaluates these methodologies across a spectrum of LLMs, consisting of prominent architectures like Llama-7B, Llama-13B, and Llama-70B, among others, through over 200 experiments conducted in diverse scenarios.

Key Findings and Contributions

1. Effectiveness of Backdoor Attacks:
   • The paper provides empirical evidence establishing the feasibility and effectiveness of backdoor attacks across various LLMs. Backdoor triggers notably increase the attack success rate (ASR) significantly beyond what isolated jailbreak attempts can achieve in the absence of such backdoors.
2. Attack Methods:
   • By conducting systematic evaluations of different data poisoning attack methods (DPA) such as BadNets, VPI, and others, and weight poisoning attacks (WPA) exemplified by BadEdit, the authors demonstrate distinct model vulnerabilities to these techniques. Larger models, for instance, tend to show heightened resilience against weight poisoning compared to smaller-scale models, which is evidenced by lower ASR in the face of similar attacks.
3. Chain-of-Thought and Hidden State Attacks:
   • Results from Chain-of-Thought Attacks (CoTA), particularly the BadChain method, underscore that improved reasoning capabilities in LLMs could potentially render them more susceptible to backdoor vulnerabilities. Conversely, the Hidden State Attacks, while promising, revealed limitations in terms of scalability and required optimal intervention strength for success.
4. Widely Applicable Benchmark:
   • BackdoorLLM emerges as a versatile benchmark that, aside from supporting attack strategies, aims to inform the development of more thorough defense mechanisms against LLM backdoor vulnerabilities.

Implications and Future Directions

The paper's implications extend to both theoretical explorations of LLM security flaws and practical concerns regarding the deployment of AI systems in sensitive applications. A robust understanding of how backdoors can be introduced and the circumstances under which they are most effective offer AI researchers and practitioners valuable insights to bolster AI safety measures.

An intriguing avenue for future research lies in the refinement and development of defensive techniques. Despite the comprehensive benchmarking of backdoor attacks, the authors acknowledge the deficiency in equally robust defense strategies within their current framework. Thus, a logical progression would be the integration of defense methodologies within BackdoorLLM, fostering a cohesive environment for advancing both offensive and defensive strategies in AI security.

In summary, "BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks on LLMs" makes substantial contributions to the field by not only delineating the attack landscape on LLMs but also opening new frontiers for enhancing AI model resilience in the face of adversarial threats. Researchers and developers alike would benefit from engaging with this benchmark for a deeper exploration of secure AI deployment methodologies.