[2606.03453] FORGE: Multi-Agent Graduated Exploitation and Detection Engineering

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Computer Science > Cryptography and Security

arXiv:2606.03453 (cs)

[Submitted on 2 Jun 2026]

Title:FORGE: Multi-Agent Graduated Exploitation and Detection Engineering

Authors:Farooq Shaikh<br>View a PDF of the paper titled FORGE: Multi-Agent Graduated Exploitation and Detection Engineering, by Farooq Shaikh

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Abstract:Vulnerability disclosure volumes now far exceed organizational assessment capacity, yet three adjacent research communities (proof-of-concept generation, vulnerability prioritization, and detection rule engineering) operate largely in isolation. Existing automated exploit generation systems report binary pass/fail outcomes, discarding partial progress and producing no signal for the other two communities. This paper presents FORGE, a multi-agent system that bridges these three silos through graduated exploitation depth. Five specialized agents (Intel, Generator, Planner, Exploit, and Detector) execute in a fixed pipeline that (1) generates targeted vulnerable applications from CVE metadata, (2) conducts coached, multi-turn exploitation assessed by an LLM-primary oracle on a four-level taxonomy (L0: no evidence through L3: full compromise), and (3) produces Sigma and Snort detection rules grounded in OpenTelemetry exploitation traces. Graduated depth is the bridging mechanism: deeper exploitation yields richer behavioral traces for detection engineering, while depth data across scoring bands provides ground truth for prioritization validation. A tiered knowledge architecture accumulates intelligence across assessments, transferring build and exploitation experience to subsequent CVEs. Evaluation on 603 CVEs from the CVE-GENIE dataset achieves 67.8% end-to-end L1+ exploitation at USD 1.50 per CVE across eight languages and 187 CWE types. Exploitation rates remain near 68% regardless of EPSS or CVSS band, indicating that pattern-level reachability is orthogonal to metadata-based prioritization. Detection rules from L2+ exploitation achieve significantly higher span-normalized grounding than L1-derived rules (p=0.035), and 93.4% of generated Snort rules produce zero false positives against a synthetic benign corpus.

Comments:<br>18 pages, 4 figures, 3 tables. Accepted at the AgentCy Workshop at the 21st International Conference on Availability, Reliability and Security (ARES 2026). Keywords: Vulnerability assessment, Multi-agent systems, Exploit generation, Detection engineering, Risk prioritization

Subjects:

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

Cite as:<br>arXiv:2606.03453 [cs.CR]

(or<br>arXiv:2606.03453v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2606.03453

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Farooq Shaikh [view email]<br>[v1]<br>Tue, 2 Jun 2026 10:32:28 UTC (81 KB)

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