FlowHijack is the first dynamics-aware backdoor attack on flow-matching VLAs that achieves high success rates with stealthy triggers while preserving benign performance and making malicious actions kinematically indistinguishable from normal ones.
arXiv preprint arXiv:2408.02882 , year=
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TimeGuard defends time series forecasting against backdoors via channel-wise pool training initialized by time-aware criteria and expanded with distance-regularized loss selection, improving poisoned MAE by 1.96x while keeping clean MAE within 5%.
ROME generates deceptive safety benchmarks that degrade LLM agent judgment performance, while ARISE uses analogical retrieval to improve safety decisions at inference time without retraining.
GuardAD reduces accident rates by 32% in autonomous driving MLLMs by using n-th order Markovian logic to infer latent hazards and revise actions.
A multi-level taxonomy of risks, attacks, and defenses across the full embodied AI pipeline, synthesizing 500+ papers and flagging overlooked failure modes.
A patch-augmented cross-view regularization method reduces backdoor attack success rates in multimodal LLMs by enforcing output differences between original and perturbed views while using entropy constraints to preserve benign generation quality.
citing papers explorer
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FlowHijack: A Dynamics-Aware Backdoor Attack on Flow-Matching Vision-Language-Action Models
FlowHijack is the first dynamics-aware backdoor attack on flow-matching VLAs that achieves high success rates with stealthy triggers while preserving benign performance and making malicious actions kinematically indistinguishable from normal ones.
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TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting
TimeGuard defends time series forecasting against backdoors via channel-wise pool training initialized by time-aware criteria and expanded with distance-regularized loss selection, improving poisoned MAE by 1.96x while keeping clean MAE within 5%.
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Enhancing Agent Safety Judgment: Controlled Benchmark Rewriting and Analogical Reasoning for Deceptive Out-of-Distribution Scenarios
ROME generates deceptive safety benchmarks that degrade LLM agent judgment performance, while ARISE uses analogical retrieval to improve safety decisions at inference time without retraining.
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GuardAD: Safeguarding Autonomous Driving MLLMs via Markovian Safety Logic
GuardAD reduces accident rates by 32% in autonomous driving MLLMs by using n-th order Markovian logic to infer latent hazards and revise actions.
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Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses
A multi-level taxonomy of risks, attacks, and defenses across the full embodied AI pipeline, synthesizing 500+ papers and flagging overlooked failure modes.
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A Patch-based Cross-view Regularized Framework for Backdoor Defense in Multimodal Large Language Models
A patch-augmented cross-view regularization method reduces backdoor attack success rates in multimodal LLMs by enforcing output differences between original and perturbed views while using entropy constraints to preserve benign generation quality.