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Capability-Grounded Agentic Planning (CGAP) is presented, which compiles the tool catalog deterministically into a PDDL model, uses an LLM to turn the request into goals, and solves the model with a classical planner.
This work introduces RoboAuto, a framework for robot program search that explicitly organizes physical understanding at the object, scene, and interaction levels through three components and shows that the knowledge base transfers across simulators and embodiments, and that program search performance scales with its si...
This work introduces AutoRedClaw, an automated red-teaming framework that generates end-to-end attacks together with the environments needed to execute them, and constructs RedClaw-Bench, a 330-case benchmark that yields substantially higher attack success rates (ASRs) than AgentDojo, AgentDyn, and DTap.
Red-TTT is proposed, which updates the attacker's parameters during the attack on each behavior, and requires only sampling access to the victim and integrates into existing attack pipelines with no other changes, increasing attack success rate and cracking many behaviors previous method cannot.
Large language model (LLM) agents are increasingly deployed in tool-augmented environments, but their reliance on external inputs makes them highly vulnerable to prompt injection attacks that can hijack task objectives. Existing safety alignment methods rely on static expert trajectories or preference optimization, lim...
AutoDojo is introduced, a generative benchmark built on AgentDojo and AgentDyn that generates IPI adaptively for a given agent and defense, and it is demonstrated that standard static benchmarks often significantly overestimate defense efficacy.
Internal representations thus support cross-mechanism attack-exposure auditing against calibrated benign references, but decouple from downstream task compromise and parameter integrity, according to downstream task compromise and parameter integrity.
It is argued that anomaly detection for agentic AI must reason at the workflow level, where global execution structure exposes signals that local checks cannot see, and presents Skynet, a principled workflow-level anomaly detection framework that turns observed multi-agent execution into directed workflow graphs and sc...
ROPE is presented, which is anchored in a structural notion of trust, and holds attack success rate to 1.6--2.6 while retaining 82--100% of undefended clean utility, significantly exceeding state-of-the-art system-level defenses in utility while attaining comparable or better security.
TokenPilot is presented, a dual-granularity context management framework that reduces costs by 61% and 56% in isolated mode, and 61% and 87% in continuous mode, while maintaining competitive performance compared to prior systems.
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