Hidden State Poisoning Attacks against Mamba-based Language Models
The emergence of State Space Models (SSMs) like Mamba has introduced an efficient alternative to traditional Transformer-based language models, boasting linear time complexity that enhances performance in various natural language processing tasks. Despite their advantages, the adversarial robustness of these models has not been thoroughly investigated, raising concerns about their vulnerability to specific attacks. A recent study published on arXiv sheds light on a critical form of attack known as Hidden State Poisoning Attack (HiSPA), revealing significant implications for the security of SSMs.
Understanding Hidden State Poisoning Attacks
HiSPA refers to a phenomenon where particular short input phrases can induce a partial amnesia effect in language models. This attack irreversibly overwrites information stored in the hidden states of the models, leading to significant degradation in their performance. The research introduces a benchmarking tool called RoBench-25, designed to assess a model’s capability to retrieve information when subjected to HiSPAs. Findings from this benchmark demonstrate that SSMs, including Mamba, are particularly susceptible to these types of adversarial attacks.
Key Findings and Implications
The study reveals several critical findings regarding the vulnerabilities of Mamba-based models:
- Performance Collapse: Even advanced models such as Jamba-1.7-Mini, a hybrid SSM-Transformer model with 52 billion parameters, exhibited a collapse in performance when subjected to certain HiSPA triggers on the RoBench-25 benchmark.
- Weakened Capabilities: The Jamba model, when tested against the well-known Open-Prompt-Injections benchmark, showed significant weakening due to HiSPA triggers, highlighting the inherent vulnerabilities in SSMs compared to pure Transformer models.
- Broader Impact: The theoretical and empirical findings extend beyond Jamba-1.7-Mini to include newer models like Mamba-2 and hybrid architectures such as Nemotron-3-Nano, suggesting a widespread issue across various implementations of SSMs.
Interpretability and Mitigation Strategies
One of the notable aspects of the research is its focus on interpretability. Through an intensive study of Mamba’s hidden layers during instances of HiSPA, researchers identified patterns that may aid in the development of mitigation strategies. Understanding how hidden states are affected by specific triggers could pave the way for constructing systems designed to defend against such attacks, thereby enhancing the robustness of SSMs.
Conclusion
The findings presented in this study underscore the urgent need for addressing the adversarial vulnerabilities of Mamba-based language models. As SSMs gain traction in the field of natural language processing, ensuring their robustness against attacks like HiSPA is imperative. The research not only highlights the susceptibility of these models but also opens avenues for further exploration into interpretability and defense mechanisms.
For those interested in the detailed methodologies and results, the full code and data necessary to reproduce the experiments are available at https://anonymous.4open.science/r/hispa_anonymous-5DB0.
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