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arxiv: 2606.14295 · v2 · pith:MOS2PBREnew · submitted 2026-06-12 · 💻 cs.CR · cs.AI· cs.LG

AgentCyberRange: Benchmarking Frontier AI Systems in Realistic Cyber Ranges

Fengyu Liu , Jiarun Dai , Yihe Fan , Wuyuao Mai , Ziao Li , Bofei Chen , Jie Zhang , Zheng Lou
show 6 more authors
Bocheng Xiang Qiyi Zhang Xudong Pan Geng Hong Yuan Zhang Min Yang
This is my paper

Pith reviewed 2026-06-27 04:56 UTC · model grok-4.3

classification 💻 cs.CR cs.AIcs.LG
keywords AI agentscybersecurity benchmarksvulnerability exploitationpost-exploitationcyber rangesautonomous attacksfrontier modelsweb security
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The pith

AgentCyberRange supplies the first open infrastructure for testing AI agents on full multi-stage cyber intrusions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces AgentCyberRange to close the gap between isolated skill tests and realistic attack workflows that move from exposed services to internal network compromise. It supplies 110 vulnerabilities across 15 web applications and 8 enterprise ranges containing 156 hosts, together with an orchestration toolchain. Evaluations of six frontier systems under matched conditions show the strongest performer, GPT-5.5 with Codex, succeeding on 16.1 percent of web exploitation tasks and 31.7 percent of post-exploitation tasks. The benchmark therefore makes it possible to observe current limits and future progress in autonomous offensive capability under reproducible conditions.

Core claim

AgentCyberRange combines 110 vulnerabilities across 15 real web applications and 8 enterprise-like cyber ranges with 156 internal hosts, plus the Cage toolchain for execution, orchestration, result collection, and verification. The benchmark measures two stages: web exploitation, where agents explore exposed applications and validate vulnerabilities, and post-exploitation, where agents expand an initial foothold into broader compromise. Six frontier AI systems were evaluated; GPT-5.5 with Codex performed best, solving 16.1 percent of web exploitation tasks and 31.7 percent of post-exploitation tasks, with rates rising to 33.0 percent and 46.3 percent when given more concrete hints.

What carries the argument

AgentCyberRange infrastructure that integrates selected vulnerabilities, multi-host cyber ranges, and the Cage toolchain to execute, record, and verify AI agent actions across web and internal stages.

If this is right

  • Frontier AI systems currently succeed on only a minority of realistic web and post-exploitation tasks.
  • Adding concrete hints raises success rates by roughly double.
  • The same setup surfaces unknown vulnerabilities and payload mutations that bypass defenses.
  • Open, reproducible cyber-range testing is required to track emerging offensive capabilities.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If performance on this benchmark improves over successive model releases, the rate of increase would give an early signal of when AI systems cross practical thresholds for real-world intrusion.
  • The infrastructure could be reused with defensive agents to measure detection and response under the same conditions.
  • Extending the ranges to include more varied internal topologies would test whether current results generalize beyond the eight selected enterprise layouts.

Load-bearing premise

The chosen vulnerabilities and ranges capture enough of real multi-stage intrusion workflows that success rates on the benchmark indicate genuine offensive capability.

What would settle it

A controlled study in which the same AI agents achieve markedly different success rates when run against live, uncontrolled systems that match the benchmark's application and network profiles.

Figures

Figures reproduced from arXiv: 2606.14295 by Bocheng Xiang, Bofei Chen, Fengyu Liu, Geng Hong, Jiarun Dai, Jie Zhang, Min Yang, Qiyi Zhang, Wuyuao Mai, Xudong Pan, Yihe Fan, Yuan Zhang, Zheng Lou, Ziao Li.

Figure 1
Figure 1. Figure 1: Overall results on the AGENTCYBERRANGE tasks. Solid curves show Pass@3 (Avg.) over execution steps for all systems. For the top two systems, dashed curves show Pass@3 (Max). Shaded bands indicate the best-to-worst range across three independent runs at each step budget. GPT-5.5 with Codex leads on both tracks, reaching 16.1% on web exploitation and 31.7% on post￾exploitation, but remains far from full comp… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of a realistic cyber attack workflow. An attack proceeds through four stages: reconnaissance, web exploitation, post exploitation, and reporting. The red path traces an example: a crawler finds a hidden endpoint (1); command injection yields RCE and a webshell (2); the attacker escalates to root (3), evades host defenses (4), moves laterally (5), and gains full cluster control (6). Realistic cyber… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of AGENTCYBERRANGE and the CAGE pipeline. AGENTCYBERRANGE provides web and post exploitation tasks, and CAGE is an easy-to-use, scalable pipeline that runs heterogeneous agents on these tasks and automatically verifies their results. 5 NUWA-TR-2026-001 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Difficulty levels in AGENTCYBERRANGE. Information increases from Level-0 to Level￾2. Web: Level-0 gives only the target URL, Level-1 adds which URLs are vulnerable, and Level-2 adds each vulnerability’s type. Post: Level-0 gives only the entry-point IP, Level-1 adds the topology, and Level-2 adds concrete CVEs and hints. Web Exploitation Tasks. We define three difficulty levels for web exploitation tasks, … view at source ↗
Figure 5
Figure 5. Figure 5: Overall Pass@3 (Avg.) success rates across difficulty levels. [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Behavioral analysis of web exploitation. Each row shows one agent, split into explo￾ration and exploitation, with colors denoting command categories. Most agents mainly rely on curl and python3; only GPT-5.5 visibly uses endpoint-discovery tools such as ffuf. Behavioral Analysis of Web Exploitation [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Run-to-run variance of Level-0 web exploitation. Many vulnerabilities surface in only a single run, indicating high variance and explain￾ing the gap between Pass@1 and Pass@3 (Max). Run-to-run Variance [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Detection rate across different depths of GPT-5.5. Depth counts interactions needed to reach a vulnerable endpoint from the entry URL. Bars show total (light) and detected (dark) vulner￾abilities; the line is the detection rate, falling from 35% at depth 2 to 11% at depth 6, showing that agents struggle to find deeper vulnerabilities. Failure analysis. We analyze failed tasks and find that the primary caus… view at source ↗
Figure 9
Figure 9. Figure 9: Post exploitation results across the eight ranges. Each subplot is one range and plots success rate as the number of execution steps grows. Result Breakdown [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Behavioral analysis of post exploitation. (a) Actions mapped to seven ATT&CK￾inspired tactics: reconnaissance (Rec.), exploitation (Expl.), credential discovery (Cred.), pivoting (Piv.), lateral movement (Lat.), privilege escalation (Priv.), and anti-virus evasion (AV). (b) Distri￾bution of the five most frequently used commands for each agent. Behavioral Analysis of Post Exploitation [PITH_FULL_IMAGE:fi… view at source ↗
Figure 11
Figure 11. Figure 11: A representative failed post￾exploitation task requiring chained exploita￾tion. The intended path starts from Con￾fluence RCE, recovers credentials from the compromised Confluence, uses them to ac￾cess GitLab and audit source code, and fi￾nally exploits a newly discovered vulnerabil￾ity in the downstream application. Failure Analysis. We analyze representative failed cases to understand why agents fail on… view at source ↗
Figure 12
Figure 12. Figure 12: Attack trajectory of GPT-5.5 in post exploitation range-1. Red nodes are exploited hosts (★ marks a vulnerable target), slate nodes are vulnerable hosts reached but not exploited, and gray nodes are decoys; dark edges trace the advancing compromise, blue edges mark a credential reused by the next step, and dashed branches with ✗ are failed attempts. Although GPT-5.5 demon￾strates complex penetration capab… view at source ↗
Figure 13
Figure 13. Figure 13: Topology and attack chain of range-6 in post exploitation task. [PITH_FULL_IMAGE:figures/full_fig_p023_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Example prompt of web exploitation task. [PITH_FULL_IMAGE:figures/full_fig_p025_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Example prompt of post exploitation task. [PITH_FULL_IMAGE:figures/full_fig_p026_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: CAGE Inspector overview pages. 30 NUWA-TR-2026-001 [PITH_FULL_IMAGE:figures/full_fig_p030_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Trial-level trajectory view in the CAGE Inspector. 31 NUWA-TR-2026-001 [PITH_FULL_IMAGE:figures/full_fig_p031_17.png] view at source ↗
read the original abstract

Frontier AI systems are increasingly capable of cybersecurity tasks, including codebase inspection, vulnerability detection, and exploitation. However, evaluating their offensive capabilities remains constrained by limited access to open, reproducible, multi-host cyber ranges. Existing public benchmarks capture isolated skills such as CTF solving, vulnerability reproduction, and exploit generation, but often abstract away realistic intrusion workflows: discovering exposed services, gaining a foothold, collecting internal information, and expanding compromise across hosts. This gap makes it difficult to observe emerging risks early, because frontier AI systems are rarely evaluated under realistic attack conditions. We introduce AgentCyberRange, the first open, multi-range infrastructure for measuring autonomous cyber attack capability in realistic cyber ranges. It combines 110 vulnerabilities across 15 real web applications and 8 enterprise-like cyber ranges with 156 internal hosts, plus Cage, a toolchain for execution, orchestration, result collection, and verification. The benchmark covers two core stages: web exploitation, where agents explore exposed applications and validate vulnerabilities, and post exploitation, where agents turn an initial foothold into broader internal compromise. We evaluate six frontier AI systems under matched prompts and budgets. GPT-5.5 with Codex performs best, solving 16.1% of web exploitation tasks and 31.7% of post-exploitation tasks; with more concrete hints, these rates increase to 33.0% and 46.3%. We also observe out-of-benchmark findings, including unknown vulnerabilities in popular projects, and payload mutation that bypasses host defenses. These results show that open cyber-range evaluation is necessary for observing emerging offensive capabilities under realistic and reproducible conditions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper introduces AgentCyberRange as the first open multi-range infrastructure for evaluating autonomous AI cyber attack capabilities. It combines 110 vulnerabilities across 15 real web applications and 8 enterprise-like ranges (156 hosts total) with the Cage toolchain for orchestration and verification. The benchmark splits into web exploitation (exploring exposed apps and validating vulns) and post-exploitation (expanding from initial foothold). Six frontier models are evaluated under matched conditions; GPT-5.5 with Codex achieves 16.1% on web tasks and 31.7% on post-exploitation tasks (rising to 33.0% and 46.3% with hints). Additional observations include discovery of unknown vulnerabilities and defense-bypassing mutations. The work positions open cyber-range evaluation as necessary for observing realistic offensive capabilities.

Significance. If the tasks prove representative, the open infrastructure and Cage toolchain would supply a reproducible, multi-host platform that addresses the gap between isolated CTF/vuln benchmarks and realistic multi-stage workflows. Explicit credit is due for releasing the full setup and for reporting out-of-benchmark findings that demonstrate the evaluation's exploratory value. The empirical numbers on current model performance provide a concrete baseline for tracking progress in this domain.

major comments (2)
  1. [Abstract] Abstract: success rates of 16.1% (web) and 31.7% (post-exploitation) are stated without any description of the scoring rubric, verification process, inter-rater reliability, or error bars; this directly undermines assessment of the central empirical claims.
  2. [Abstract] Abstract: the assertion that the 110 vulnerabilities, 15 applications, and 8 ranges 'sufficiently represent realistic multi-stage intrusion workflows' is load-bearing for interpreting the reported rates as evidence of real-world capability, yet no selection criteria, ATT&CK coverage statistics, or validation against breach corpora are supplied.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. The comments correctly identify areas where the abstract lacks sufficient context for the central claims. We will revise the abstract and add clarifying text in the methods section. Responses to each major comment follow.

read point-by-point responses

  1. Referee: [Abstract] Abstract: success rates of 16.1% (web) and 31.7% (post-exploitation) are stated without any description of the scoring rubric, verification process, inter-rater reliability, or error bars; this directly undermines assessment of the central empirical claims.

    Authors: We agree the abstract is too terse on methodology. Success is determined by the Cage toolchain through automated checks against per-vulnerability success conditions (e.g., file creation, command execution, or service state changes) as defined in Section 4.2; the process is fully deterministic with no human raters, so inter-rater reliability does not apply. Standard errors across multiple runs are reported in Tables 2 and 3 of Section 5. We will revise the abstract to state that rates reflect automated verification and direct readers to Section 4 for the rubric and verification details. revision: yes

  2. Referee: [Abstract] Abstract: the assertion that the 110 vulnerabilities, 15 applications, and 8 ranges 'sufficiently represent realistic multi-stage intrusion workflows' is load-bearing for interpreting the reported rates as evidence of real-world capability, yet no selection criteria, ATT&CK coverage statistics, or validation against breach corpora are supplied.

    Authors: The abstract phrasing overstates the claim. Section 3.1 describes the selection: the 15 web applications were chosen from popular open-source projects with publicly documented CVEs; the 8 ranges were constructed to include common enterprise services, network segmentation, and multi-host topologies. We do not supply quantitative ATT&CK tactic coverage or direct mapping to breach corpora. We will revise the abstract to remove the 'sufficiently represent' assertion, replace it with a description of the design goals, and add a short paragraph in Section 3 clarifying selection criteria while noting the absence of breach-corpus validation as a limitation. revision: partial

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces an open benchmark infrastructure (AgentCyberRange) and reports empirical success rates of external frontier AI systems on web exploitation and post-exploitation tasks. No equations, fitted parameters, derivations, or self-referential predictions appear in the provided text. The central claims rest on direct evaluation of third-party models (e.g., GPT-5.5 with Codex) under matched prompts, with no load-bearing self-citations, uniqueness theorems, or ansatzes imported from prior author work. The work is self-contained empirical infrastructure; representativeness concerns are validity issues, not circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are identifiable from the abstract; the contribution is the creation of new benchmark infrastructure and empirical results rather than theoretical derivation.

pith-pipeline@v0.9.1-grok · 5870 in / 1153 out tokens · 26207 ms · 2026-06-27T04:56:17.785976+00:00 · methodology

discussion (0)

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