This work operationalizes contextual security through four properties that must hold jointly and be evaluated continuously across the agent's trajectory, which changes which defenses are coherent, which evaluations measure something useful, and which attack patterns evaluation can see at all.
Abstract
Agent security is widely treated as a question about action content. Defenses ask whether an instruction looks malicious. Benchmarks ask whether an agent performs a harmful sounding action. \textbf{We argue that agent security is fundamentally a contextual problem, and that the current content based framing systematically misdefines it.} A command to ``delete user data''might be a routine administrative request or a prompt injection attacking production systems, and the content alone cannot distinguish the two. Authorization context can. Across every injection task in AgentDojo and WASP, the same action is one an authenticated user would plausibly request in a routine workflow, which makes the conflation a structural property of evaluating security through content. We operationalize contextual security through four properties that must hold jointly and be evaluated continuously across the agent's trajectory. Source Authorization asks who issued the command. Task Alignment specifies the agent's authorized objective. Action Alignment evaluates whether each action serves that objective. Data Isolation governs information flows across privilege boundaries. Under this reframing, indirect prompt injection becomes a Source Authorization violation. Snapshot benchmarks are structurally incapable of evaluating Data Isolation. Existing defenses are reorganized around the property they actually approximate. The contextual reframing changes which defenses are coherent, which evaluations measure something useful, and which attack patterns evaluation can see at all.
Open Security Benchmark is presented, a framework that benchmarks agentic AI on security posture management work and surfaces a curated enterprise environment that evaluates posture investigation across two modalities: text-to-SQL over a relational snapshot and each vendor's native API over a served instance of the sam...
Gal Engelberg, Michael Arenzon, Leon Goldberg· arXiv.org· 0 citations
Agentic artificial intelligence expands the enterprise security boundary because autonomous agents can plan
tasks, retain memory, invoke tools, call APIs, and initiate business actions. Authentication at session start is therefore
insufficient when later actions may be influenced by untrusted content, poisoned memory,...
S. Suryawanshi· International Journal of Inn...· 0 citations
It is argued that agent security must be evaluated under an untrusted-model assumption: a correct system is one in which a fully prompt-injected agent still cannot exceed the authority explicitly delegated to it, and an authorization broker is implemented that closes the gap.
Panduranga Sai Varma Dantuluri, Jyotirmoy Sundi· 0 citations
NiyamAI is presented, an intent bound runtime guardrail architecture providing cryptographically verifiable execution integrity for autonomous agents and subjects its own enforcement mechanism to 18 adversarial vectors across six classes, disclosing two implementation vulnerabilities identified and remediated during de...
Aditya Katkar, Om Karkele, Kartik Mandhane et al.· 1 citation
The right unit of safety in agentic AI is the trajectory-with-checkable-evidence, not the model, and this work formalizes an Agent Trajectory Schema and Evidence Chain, state a compositional gating proposition based on standard monitor composition, and outline a research agenda.
Albus W. Ng, Yibin Han, Jusheng Zhang et al.· 1 citation
A policy algebra is proposed that defines the reliability envelope within which agent capability may be exercised and provides researchers and practitioners with formal correctness conditions, executable decision semantics, and trace evidence for building agents that are not only capable, but reliably capable.
Bhaskar Tripathi, Anurag Kumar, R. Kumar et al.· 0 citations
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