arXiv subjects

Alireza Lotfi

Publications and source records attributed to Alireza Lotfi.

3 recordsLinked to original sources

arXiv2026

A2ABreak: Systematic Security Analysis of the A2A Protocol

The Agent2Agent (A2A) protocol, now governed by the Linux Foundation, is an open standard that enables autonomous AI agents to discover, authenticate with, and delegate tasks to one another across organizational boundaries. Designed to complement the Model Context Protocol (MCP) for tool integration, A2A is rapidly emerging as the horizontal communication layer of the multi-agent ecosystem. Yet the protocol's security has received no systematic analysis. This paper presents A2ABreak, the first rigorous systematic security analysis of the A2A protocol. We introduce a novel framework that utilizes an LLM-assisted extraction of a verified finite-state machine directly from the natural-language specification, producing a unified model of 37 states and 76 transitions from 929 formalized statements, and then systematically reasons over this model to discover protocol-level vulnerabilities through adversarial verification, under a full-compliance assumption. Our analysis uncovers 11 new vulnerabilities, each exploitable by a specification-compliant adversary without requiring any implementation flaw. Among the findings are cross-client context injection through unprotected context identifiers, credential harvesting via multi-hop identity loss in delegation chains, and data exfiltration through rogue agents advertising unattested capability claims. A2ABreak achieves 73.3% precision and 84.6% F1 against independent expert review, while a zero-shot LLM baseline operating over the same specification produces zero confirmed findings, demonstrating that explicit formal grounding is essential for sound protocol security analysis.

cs.CR↗

arXiv2026

Securing Agentic AI: From Per-Action Checks to Trajectory Assurance

Autonomous agents are increasingly used to execute consequential tasks in environments governed by operational constraints, organizational policies, regulatory requirements, and technical standards. Their safety is therefore determined not by the correctness of individual actions, but by whether their overall behavior remains consistent with the rules and invariants of the systems in which they operate. As large language model (LLM)-based agents become more autonomous and increasingly delegate tasks across organizational boundaries, securing them evolves from a single challenge into a broad and interconnected landscape spanning the entire agentic stack. At the single-agent level, untrusted inputs through prompts, memory, retrieved knowledge, and tool interfaces create attack surfaces. In multi-agent settings, delegation and communication introduce challenges related to identity, trust, capability control, and decision transparency, while the underlying model routing and execution control plane remains vulnerable to manipulation and to unverified model provenance. Perhaps the most fundamental challenge is behavioral containment: sequences of individually permissible actions may collectively violate system-level constraints and safety invariants. At the broader level, supply-chain integrity, provenance, accountability, and end-to-end observability remain largely open problems. A common principle unifies these directions: security must become a verifiable property of the architectures, protocols, and runtimes that govern agent behavior, rather than an optional layer of guidance. Charting these challenges provides a roadmap toward trustworthy autonomous agent deployment.

cs.AI↗

arXiv2025

Automated Vulnerability Validation and Verification: A Large Language Model Approach

Software vulnerabilities remain a critical security challenge, providing entry points for attackers into enterprise networks. Despite advances in security practices, the lack of high-quality datasets capturing diverse exploit behavior limits effective vulnerability assessment and mitigation. This paper introduces an end-to-end multi-step pipeline leveraging generative AI, specifically large language models (LLMs), to address the challenges of orchestrating and reproducing attacks to known software vulnerabilities. Our approach extracts information from CVE disclosures in the National Vulnerability Database, augments it with external public knowledge (e.g., threat advisories, code snippets) using Retrieval-Augmented Generation (RAG), and automates the creation of containerized environments and exploit code for each vulnerability. The pipeline iteratively refines generated artifacts, validates attack success with test cases, and supports complex multi-container setups. Our methodology overcomes key obstacles, including noisy and incomplete vulnerability descriptions, by integrating LLMs and RAG to fill information gaps. We demonstrate the effectiveness of our pipeline across different vulnerability types, such as memory overflows, denial of service, and remote code execution, spanning diverse programming languages, libraries and years. In doing so, we uncover significant inconsistencies in CVE descriptions, emphasizing the need for more rigorous verification in the CVE disclosure process. Our approach is model-agnostic, working across multiple LLMs, and we open-source the artifacts to enable reproducibility and accelerate security research. To the best of our knowledge, this is the first system to systematically orchestrate and exploit known vulnerabilities in containerized environments by combining general-purpose LLM reasoning with CVE data and RAG-based context enrichment.

cs.CR↗