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SafeFlow reveals that multi-agent systems can obscure malicious intent through task decomposition, but a semantic information-flow approach can effectively counteract this vulnerability.
SafeGen boosts VLMAD performance by over 24% in safety-critical scenario generation, bridging the sim-to-real gap that has long plagued autonomous driving systems.
Adversarial attacks on vision-language agents reveal critical vulnerabilities, with multi-view optimization strategies proving significantly more effective than isolated approaches.
Environmental illusions can degrade lane detection accuracy by over 7%, posing serious safety risks for autonomous vehicles.
