Zonghao Ying

Indirect prompt injection can compromise AI systems like DeepSeek Harness, with attack success rates reaching up to 25.5% under certain conditions.

Extreme fidelity loss reveals critical vulnerabilities in long-horizon tasks that standard accuracy metrics overlook.

SkillSentry reveals that dynamic testing can uncover harmful agent behaviors that static analysis fails to detect, achieving unprecedented accuracy in safety evaluations.

SkillJack reveals that self-evolving agents can unknowingly incorporate malicious skills, making traditional safety measures ineffective against persistent threats.

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.

MIND redefines adversarial prompt generation, achieving a staggering 95.62% success rate by intelligently interpreting model defenses rather than relying on brute-force tactics.

Adversarial attacks on vision-language agents reveal critical vulnerabilities, with multi-view optimization strategies proving significantly more effective than isolated approaches.

AI-Infra-Guard reveals that a unified security framework can effectively address the diverse attack surfaces of AI agents, making it a game-changer for AI safety.