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Malicious actors can stealthily embed architectural backdoors in VLMs, compromising their integrity while keeping normal functionality intact.
AI-based malware detectors that rely on domain knowledge significantly outperform deep learning models in long-term resilience and security against adversarial attacks.
Attack ensembles optimized for minimum-norm strategies can provide a more accurate and flexible evaluation of adversarial robustness than traditional fixed-budget methods.
Existing problem-space attacks are largely impractical, but DROIDBREAKER shows how to craft adversarial APKs that evade detection while preserving functionality.
Modeling prompt embeddings in hyperbolic space enables lightweight, geometry-aware detection of harmful VLM prompts that outperforms existing defenses.
