CORTEXA
← Browse
arxivcs.CRcs.AI2026-07-23

Toward cryptographically verifiable authorization for autonomous AI agents: A security hypothesis, preliminary formal model, and proof-of-concept implementation

M. Llambí-Morillas, D. Fernández-Fernández

Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight. Existing authentication and authorization mechanisms establish identity and delegate authority, but do not inherently provide cryptographic evidence that a concrete request issued by a specific agent satisfies the applicable policy in a specific execution context. This paper hypothesizes that agent authorization can be formalized as a cryptographically verifiable relation, denoted $R_{CVA}$, that jointly binds an agent principal, a concrete authorization request, an execution context, and the satisfaction of an applicable policy, while selectively preserving the confidentiality of private authorization attributes. We introduce a preliminary formal abstraction for Cryptographically Verifiable Agent Authorization (CVA), define a compact set of candidate security properties including authorization soundness, principal binding, request binding, policy binding, and replay resistance, and provide an executable zero-knowledge proof of concept that instantiates selected elements of the model over a Groth16 zk-SNARK construction. We further identify and formalize the structural separation among identity binding, authorization-request binding, and runtime execution binding as a central open problem in the design of secure agentic systems (a distinction {not explicitly addressed by} current agentic security frameworks) and present a falsifiable research agenda for its resolution.

View free PDFSource page

Related papers

arxivcs.CRcs.AI2026-07-24

Agent Security Needs Redefinition through a Holistic Framework

Vincent Siu, Jingxuan He, Kyle Montgomery, Zhun Wang, Chenguang Wang, Dawn Song

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 c…

View free PDFSource page
arxivcs.CRcs.AI2026-07-31

MOSAIC: Masked Outsourcing of Secure AI Computations

James Hsin-yu Chiang, Sheila Zingg, Kari Kostiainen, Srdjan Capkun

We address the challenge of securely and efficiently outsourcing AI computations from a trusted but computationally weak client to an untrusted but powerful server, in the setting where the client holds both the input and the model, and the server must learn neither. We present M…

View free PDFSource page
arxivcs.AIcs.CR2026-07-24

Dynamic Capability Scoping for Enterprise AI Agents: A Synthetic Dataset and Three-Source Permission Architecture

Halil Burak Noyan

Enterprise AI agents are typically granted static credential sets at configuration time, holding every tool the role might need for every task they perform. This persistent over-privilege expands the attack surface. We argue that capability scoping must follow a dynamic least-pri…

View free PDFSource page
arxivcs.SEcs.AIcs.CR2026-07-31

AgenticRepair: Multi-Faceted Program Context Engineering for Agentic Vulnerability Repair

Michael Fu, Qiyue Mei, Patanamon Thongtanunam, Kla Tantithamthavorn

Automated vulnerability repair aims to reduce the time and effort required to patch security flaws from a vulnerability triage report. Recent agentic AI approaches have shown promising results in automated program repair. However, vulnerability repair demands richer program conte…

View free PDFSource page