ShieldNet detects supply-chain poisoned tools in LLM agents by monitoring network interactions with a MITM proxy and lightweight classifier, reaching 0.995 F1 and 0.8% false positives on a new benchmark of 25+ attack types.
Advagent: Controllable blackbox red- teaming on web agents.arXiv preprint arXiv:2410.17401
7 Pith papers cite this work. Polarity classification is still indexing.
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Introduces an app-content instrumentation framework and benchmark showing that examined GUI agents suffer 42.0% and 36.1% average misleading rates from third-party content in dynamic and static tests respectively.
Progent introduces a privilege-control framework for AI agents that uses LLM-generated symbolic rules over tools, SMT-solver-enforced monotonic updates, and deterministic checks to reduce attack success rates on AgentDojo and ASB benchmarks.
A survey that defines Computer-Using Agents for safety analysis, categorizes their threats, proposes a taxonomy of defensive strategies, and summarizes benchmarks and datasets for evaluating CUA safety and performance.
A survey that taxonomizes threats to agentic AI, reviews benchmarks and evaluation methods, discusses technical and governance defenses, and identifies open challenges.
LLM agents enable universal interoperability by serving as automatic translators and adapters between proprietary digital services.
citing papers explorer
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ShieldNet: Network-Level Guardrails against Emerging Supply-Chain Injections in Agentic Systems
ShieldNet detects supply-chain poisoned tools in LLM agents by monitoring network interactions with a MITM proxy and lightweight classifier, reaching 0.995 F1 and 0.8% false positives on a new benchmark of 25+ attack types.
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Mobile GUI Agents under Real-world Threats: Are We There Yet?
Introduces an app-content instrumentation framework and benchmark showing that examined GUI agents suffer 42.0% and 36.1% average misleading rates from third-party content in dynamic and static tests respectively.
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Progent: Securing AI Agents with Privilege Control
Progent introduces a privilege-control framework for AI agents that uses LLM-generated symbolic rules over tools, SMT-solver-enforced monotonic updates, and deterministic checks to reduce attack success rates on AgentDojo and ASB benchmarks.
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A Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron?
A survey that defines Computer-Using Agents for safety analysis, categorizes their threats, proposes a taxonomy of defensive strategies, and summarizes benchmarks and datasets for evaluating CUA safety and performance.
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Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges
A survey that taxonomizes threats to agentic AI, reviews benchmarks and evaluation methods, discusses technical and governance defenses, and identifies open challenges.
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LLM Agents Are the Antidote to Walled Gardens
LLM agents enable universal interoperability by serving as automatic translators and adapters between proprietary digital services.
- SkillTrojan: Backdoor Attacks on Skill-Based Agent Systems