ESAA-Security: An Event-Sourced, Verifiable Architecture for Agent-Assisted Security Audits of AI-Generated Code

• URL: http://arxiv.org/abs/2603.06365v1
• Date: Fri, 06 Mar 2026 15:15:26 GMT
• Title: ESAA-Security: An Event-Sourced, Verifiable Architecture for Agent-Assisted Security Audits of AI-Generated Code
• Abstract summary: This paper presents ESAA-Security, a domain-specific framework for agent-assisted security auditing of software repositories.<n>The framework produces structured check results, vulnerability inventories, severity classifications, risk, remediation guidance, executive summaries, and a final markdown/JSON audit report.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-assisted software generation has increased development speed, but it has also amplified a persistent engineering problem: systems that are functionally correct may still be structurally insecure. In practice, prompt-based security review with large language models often suffers from uneven coverage, weak reproducibility, unsupported findings, and the absence of an immutable audit trail. The ESAA architecture addresses a related governance problem in agentic software engineering by separating heuristic agent cognition from deterministic state mutation through append-only events, constrained outputs, and replay-based verification. This paper presents ESAA-Security, a domain-specific specialization of ESAA for agent-assisted security auditing of software repositories, with particular emphasis on AI-generated or AI-modified code. ESAA-Security structures auditing as a governed execution pipeline with four phases reconnaissance, domain audit execution, risk classification, and final reporting and operationalizes the workflow into 26 tasks, 16 security domains, and 95 executable checks. The framework produces structured check results, vulnerability inventories, severity classifications, risk matrices, remediation guidance, executive summaries, and a final markdown/JSON audit report. The central idea is that security review should not be modeled as a free-form conversation with an LLM, but as an evidence-oriented audit process governed by contracts and events. In ESAA-Security, agents emit structured intentions under constrained protocols; the orchestrator validates them, persists accepted outputs to an append-only log, reprojects derived views, and verifies consistency through replay and hashing. The result is a traceable, reproducible, and risk-oriented audit architecture whose final report is auditable by construction.

Related papers

• AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation [48.55515767840701]
  "AI slop" is artifacts, hallucinated vulnerabilities, plausible but incorrect patches, and semantically repackaged bug reports.<n>This paper surveys the empirical evidence, identifies a unifying mechanism, and traces a path toward trustworthy triage.
  arXiv  Detail & Related papers  (2026-08-26T11:48:38Z)
• Large Language Models at the Intersection of Software Engineering and Software Security:An Evidence-Centered Structured Survey and Research Agenda [8.73908281393875]
  Large Language Models (LLMs) are moving from code completion toward repository-scale agents that retrieve context, edit files, execute tools, and participate in security-sensitive provenance.<n>This evidence-centered structured survey synthesizes representative work available through May 31, 2026 across software engineering tasks, software security tasks, adaptation mechanisms, artifact granularity, and evaluation design.
  arXiv  Detail & Related papers  (2026-08-21T13:54:38Z)
• Combating Knowledge Corruption in Agent Systems: A Byzantine-Tolerant Secure Collaborative RAG Framework [61.79960727298522]
  We propose SecureCollaRAG, a Byzantine-tolerant collaborative RAG framework.<n>Our approach enables agent system to securely verify document provenance through dynamic GNN-based credibility scoring.<n>Through extensive evaluations and formal analysis, we demonstrate that SecureCollaRAG maintains robustness against attackers under non-IID data distributions.
  arXiv  Detail & Related papers  (2026-08-05T02:13:37Z)
• SeClaw: Spec-Driven Security Task Synthesis for Evaluating Autonomous Agents [87.26967184869198]
  SeClaw is a framework that combines specification-driven security task synthesis with execution-based security evaluation for Autonomous agents.<n> benchmark covers risks arising from resources, user tasks, environments, and intrinsic agent behaviors.
  arXiv  Detail & Related papers  (2026-06-01T14:23:42Z)
• An Organization-Scoped LLM Agent Runtime Architecture for Regulated Cybersecurity Operations [0.3013679260442808]
  Regulated cybersecurity lack a runtime substrate that enforces organization-level scope.<n>Recent large language model (LLM) agent systems report strong results on isolated cybersecurity tasks.<n>This paper proposes an organization-scoped agent runtime architecture for financial cybersecurity.
  arXiv  Detail & Related papers  (2026-05-28T21:51:38Z)
• The Authorization-Execution Gap Is a Major Safety and Security Problem in Open-World Agents [111.54911637435269]
  The Authorization-Execution Gap (AEG) is a major safety and security problem in open-world agents.<n>The AEG is the divergence between what a principal intends to authorize and what an open-world agent ultimately executes.
  arXiv  Detail & Related papers  (2026-05-10T04:05:31Z)
• AgentReputation: A Decentralized Agentic AI Reputation Framework [3.6704226968275253]
  We propose textbfAgentReputation, a decentralized, three-layer reputation framework for agentic AI systems.<n>The framework separates task execution, reputation services, and tamper-proof persistence to both leverage their respective strengths and enable independent evolution.<n>In quantifying, AgentReputation provides a decision-facing policy engine that supports resource allocation, access control, and adaptive verification escalation.
  arXiv  Detail & Related papers  (2026-04-30T12:33:39Z)
• Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw [42.9467373594646]
  This report presents a comprehensive security analysis of the OpenClaw ecosystem.<n>We highlight critical vulnerabilities such as prompt injection-driven Remote Code Execution (RCE), sequential tool attack chains, context amnesia, and supply chain contamination.<n>To address these systemic architectural flaws, we introduce the Full-Lifecycle Agent Security Architecture (FASA)<n>This theoretical defense blueprint advocates for zero-trust agentic execution, dynamic intent verification, and cross-layer reasoning-action correlation.
  arXiv  Detail & Related papers  (2026-03-13T04:33:05Z)
• Aegis: Towards Governance, Integrity, and Security of AI Voice Agents [52.7512082818639]
  We propose Aegis, a framework for the governance, integrity, and security of voice agents.<n>We evaluate the framework through case studies in banking call centers, IT Support, and logistics.<n>We observe systematic differences across model families, with open-weight models exhibiting higher susceptibility.
  arXiv  Detail & Related papers  (2026-02-07T05:51:36Z)
• Constitutional Spec-Driven Development: Enforcing Security by Construction in AI-Assisted Code Generation [0.0]
  AI-assisted "vibe coding" enables rapid software development but introduces significant security risks.<n>We present Constitutional Spec-Driven Development, a methodology that embeds non-negotiable security principles into the specification.<n>We show that constitutional constraints reduce security defects by 73% compared to unconstrained AI generation.
  arXiv  Detail & Related papers  (2026-01-31T19:08:16Z)
• CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents [60.98294016925157]
  AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss.<n>We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content.<n>Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks.
  arXiv  Detail & Related papers  (2026-01-14T23:06:35Z)
• Towards Verifiably Safe Tool Use for LLM Agents [53.55621104327779]
  Large language model (LLM)-based AI agents extend capabilities by enabling access to tools such as data sources, APIs, search engines, code sandboxes, and even other agents.<n>LLMs may invoke unintended tool interactions and introduce risks, such as leaking sensitive data or overwriting critical records.<n>Current approaches to mitigate these risks, such as model-based safeguards, enhance agents' reliability but cannot guarantee system safety.
  arXiv  Detail & Related papers  (2026-01-12T21:31:38Z)
• Agentic AI for Autonomous Defense in Software Supply Chain Security: Beyond Provenance to Vulnerability Mitigation [0.0]
  The current paper includes an example of agentic artificial intelligence (AI) based on autonomous software supply chain security.<n>It combines large language model (LLM)-based reasoning, reinforcement learning (RL), and multi-agent coordination.<n>Results show that agentic AI can facilitate the transition to self defending, proactive software supply chains.
  arXiv  Detail & Related papers  (2025-12-29T14:06:09Z)
• MAIF: Enforcing AI Trust and Provenance with an Artifact-Centric Agentic Paradigm [0.5495755145898128]
  Current AI systems operate on opaque data structures that lack the audit trails, provenance tracking, or explainability required by emerging regulations like the EU AI Act.<n>We propose an artifact-centric AI agent paradigm where behavior is driven by persistent, verifiable data artifacts rather than ephemeral tasks.<n>Production-ready implementation demonstrates ultra-high-speed streaming (2,720.7 MB/s), optimized video processing (1,342 MB/s), and enterprise-grade security.
  arXiv  Detail & Related papers  (2025-11-19T04:10:32Z)
• AI Bill of Materials and Beyond: Systematizing Security Assurance through the AI Risk Scanning (AIRS) Framework [31.261980405052938]
  Assurance for artificial intelligence (AI) systems remains fragmented across software supply-chain security, adversarial machine learning, and governance documentation.<n>This paper introduces the AI Risk Scanning (AIRS) Framework, a threat-model-based, evidence-generating framework designed to operationalize AI assurance.
  arXiv  Detail & Related papers  (2025-11-16T16:10:38Z)
• Making LLMs Reliable When It Matters Most: A Five-Layer Architecture for High-Stakes Decisions [51.56484100374058]
  Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes.<n>This gap, driven by mutually cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector.<n>This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure.
  arXiv  Detail & Related papers  (2025-11-10T22:24:21Z)
• A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code [49.009041488527544]
  A.S.E is a repository-level evaluation benchmark for assessing the security of AI-generated code.<n>Current large language models (LLMs) still struggle with secure coding.<n>A larger reasoning budget does not necessarily lead to better code generation.
  arXiv  Detail & Related papers  (2025-08-25T15:11:11Z)
• DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents [52.92354372596197]
  Large Language Models (LLMs) are increasingly central to agentic systems due to their strong reasoning and planning capabilities.<n>This interaction also introduces the risk of prompt injection attacks, where malicious inputs from external sources can mislead the agent's behavior.<n>We propose a Dynamic Rule-based Isolation Framework for Trustworthy agentic systems, which enforces both control and data-level constraints.
  arXiv  Detail & Related papers  (2025-06-13T05:01:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.