SpectralGuard: Detecting Memory Collapse Attacks in State Space Models
arXiv:2603.12414v1 Announce Type: new Abstract: State Space Models (SSMs) such as Mamba achieve linear-time sequence processing through input-dependent recurrence, but this mechanism introduces a critical safety vulnerability. We show that the spectral radius rho(A-bar) of the discretized transition operator governs effective memory horizon: when an adversary drives rho toward zero through gradient-based Hidden State Poisoning, memory collapses from millions of tokens to mere dozens, silently destroying reasoning capacity without triggering output-level alarms. We prove an Evasion Existence Theorem showing that for any output-only defense, adversarial inputs exist that simultaneously induce spectral collapse and evade detection, then introduce SpectralGuard, a real-time monitor that tracks spectral stability across all model layers. SpectralGuard achieves F1=0.961 against non-adaptive attackers and retains F1=0.842 under the strongest adaptive setting, with sub-15ms per-token latency.
arXiv:2603.12414v1 Announce Type: new Abstract: State Space Models (SSMs) such as Mamba achieve linear-time sequence processing through input-dependent recurrence, but this mechanism introduces a critical safety vulnerability. We show that the spectral radius rho(A-bar) of the discretized transition operator governs effective memory horizon: when an adversary drives rho toward zero through gradient-based Hidden State Poisoning, memory collapses from millions of tokens to mere dozens, silently destroying reasoning capacity without triggering output-level alarms. We prove an Evasion Existence Theorem showing that for any output-only defense, adversarial inputs exist that simultaneously induce spectral collapse and evade detection, then introduce SpectralGuard, a real-time monitor that tracks spectral stability across all model layers. SpectralGuard achieves F1=0.961 against non-adaptive attackers and retains F1=0.842 under the strongest adaptive setting, with sub-15ms per-token latency. Causal interventions and cross-architecture transfer to hybrid SSM-Attention systems confirm that spectral monitoring provides a principled, deployable safety layer for recurrent foundation models.
Executive Summary
This article proposes SpectralGuard, a real-time monitor for detecting memory collapse attacks in State Space Models (SSMs). The authors demonstrate that hidden state poisoning attacks can drive the spectral radius of the discretized transition operator towards zero, causing memory collapse. They introduce the Evasion Existence Theorem, which shows that output-only defenses can be evaded by adversarial inputs. SpectralGuard achieves high F1 scores against non-adaptive and adaptive attackers, with sub-15ms per-token latency. The authors also demonstrate the effectiveness of spectral monitoring in hybrid SSM-Attention systems. This work provides a principled safety layer for recurrent foundation models, but its scalability and adaptability to various SSM architectures remain to be explored.
Key Points
- ▸ SpectralGuard detects memory collapse attacks in SSMs through real-time spectral monitoring.
- ▸ The authors introduce the Evasion Existence Theorem, which shows the limitation of output-only defenses.
- ▸ SpectralGuard achieves high F1 scores against non-adaptive and adaptive attackers.
Merits
Strength in theoretical foundation
The authors provide a rigorous theoretical framework for understanding the relationship between spectral radius and memory collapse, which is a significant contribution to the field.
Effectiveness in practice
SpectralGuard achieves high F1 scores against both non-adaptive and adaptive attackers, demonstrating its practical effectiveness in detecting memory collapse attacks.
Demerits
Limitation in scalability
The authors note that SpectralGuard's scalability and adaptability to various SSM architectures remain to be explored, which could limit its practical applications.
Assumption of gradient-based attacks
The authors assume that attacks are gradient-based, which may not always be the case in real-world scenarios, potentially limiting the generalizability of their results.
Expert Commentary
The article presents a significant contribution to the field of machine learning security, particularly in the context of State Space Models. The authors' work on SpectralGuard demonstrates a deep understanding of the theoretical foundations of SSMs and the practical implications of memory collapse attacks. However, as with any research, there are areas for improvement. The authors' assumption of gradient-based attacks may not always be realistic, and the scalability and adaptability of SpectralGuard to various SSM architectures remain to be explored. Nevertheless, this work provides a valuable foundation for further research in this area and has important implications for the development of more robust and secure machine learning models.
Recommendations
- ✓ Further research is needed to explore the scalability and adaptability of SpectralGuard to various SSM architectures.
- ✓ The authors should investigate the effectiveness of SpectralGuard against non-gradient-based attacks to improve its generalizability.