mlsanityData to 7 Oct 2026

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

arXiv 2603.06365 ↗First posted 6 Mar 2026cs.CR cs.AIcited by 1

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 6 of its 21 references that are in the map. First posted on arXiv 6 Mar 2026.

Builds on

6 of 21 references.

Closest ancestors · 1

Same direction, newest first

  1. 2026ESAA: Event Sourcing for Autonomous Agents in LLM-Based Software Engineering26 Feb

Other references · 5

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  3. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  4. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  5. 2023Grammar-Constrained Decoding for Structured NLP Tasks without Finetuningcited by 186

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

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  2. 2026Code-Augur: Agentic Vulnerability Detection via Specification Inference
  3. 2026AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection
  4. 2026Transferable Self-Evolving Playbooks for Agentic Security Auditing
  5. 2026AgentXploit: Autonomous Repository-to-Runtime Red-Teaming for AI Agents
  6. 2025Rethinking Autonomy: Preventing Failures in AI-Driven Software Engineering
  7. 2026MOSAIC-Bench: Measuring Compositional Vulnerability Induction in Coding Agents
  8. 2026Security in the Age of AI Teammates: An Empirical Study of Agentic Pull Requests on GitHub