Neural Interface Safety: Certificates Fail, Operational Audits Needed

New research exposes a critical flaw in the current safety assessment of embedded neural-interface models: formal robustness certificates, while mathematically valid, often fail to guarantee operational safety. The study demonstrates that a model's task accuracy can significantly degrade under adversarial attacks, even when its Lipschitz-style certificate remains valid. This discrepancy highlights a broader "alignment failure" where training objectives diverge from actual user welfare. To address this, the authors propose a unified empirical audit framework focusing on three key failure modes. First, "verification insufficiency" where certificates pass but task behavior degrades. Second, "proxy-fidelity divergence" where task-optimized representations harm the underlying neural signal structure. Third, "latent information exfiltration" where public-task embeddings inadvertently retain private user attributes. The framework was instantiated and validated on multiple EEG datasets and decoders, revealing that the verification gap is architecture-independent and emphasizing the necessity of operational safety auditing beyond mere certificate verification for responsible deployment.