arxiv: 2605.04499 · v1 · submitted 2026-05-06 · 💻 cs.CR · cs.AI

Recognition: 2 theorem links

· Lean Theorem

Pen-Strategist: A Reasoning Framework for Penetration Testing Strategy Formation and Analysis

Yasod Ginige , Pasindu Marasinghe , Sajal Jain , Suranga Seneviratne

Authors on Pith no claims yet

Pith reviewed 2026-05-08 17:44 UTC · model grok-4.3

classification 💻 cs.CR cs.AI

keywords penetration testingautomated pentestingreasoning modelfine-tuningLLM agentsstrategy derivationcybersecurity automation

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The pith

Pen-Strategist improves automated penetration testing by deriving strategies through logical reasoning and converting them to precise actions.

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

The paper addresses the shortage of cybersecurity experts by automating penetration testing more effectively. Existing LLM agents fall short in strategy formulation and tool selection. Pen-Strategist constructs a domain-specific reasoning dataset and fine-tunes a large language model using reinforcement learning to generate strategies with logical explanations. It pairs this with a classifier that turns strategies into executable steps. When integrated into frameworks like PentestGPT, it shows marked gains in performance metrics.

Core claim

A domain-specific reasoning model fine-tuned on a custom dataset of logical explanations for pentesting scenarios derives more effective strategies, and when combined with a semantic CNN classifier for step selection, boosts performance in automated penetration testing tasks compared to standard large language models.

What carries the argument

Fine-tuned Qwen-3-14B model trained with reinforcement learning on a reasoning dataset for strategy generation, together with a CNN classifier for predicting actionable steps.

Load-bearing premise

The specific vulnerable machines and custom dataset used for evaluation accurately reflect the variety and complexity of real-world penetration testing scenarios.

What would settle it

Running the integrated Pen-Strategist framework on a new set of vulnerable machines outside the original test environment and observing whether the 47.5% improvement in subtask completion persists.

Figures

Figures reproduced from arXiv: 2605.04499 by Pasindu Marasinghe, Sajal Jain, Suranga Seneviratne, Yasod Ginige.

**Figure 1.** Figure 1: System overview of the Pen-Strategist. The frame view at source ↗

**Figure 2.** Figure 2: Dataset collection steps (a) and a sample data point (b). view at source ↗

**Figure 4.** Figure 4: Distribution of the strategies in different stages of view at source ↗

**Figure 5.** Figure 5: Strategy model training steps. user prompt includes the PTT, which summarizes the attack environment up to the current stage and includes executed steps and their outcomes, along with the immediate previous step and its result. Based on this information, the model is guided to generate the next strategy through logical reasoning followed by a concise explanation. The detailed prompts used are provided in … view at source ↗

**Figure 6.** Figure 6: A sample strategy derived by Qwen-3-14B base view at source ↗

**Figure 7.** Figure 7: Subtask completion rates for HTB machines. P, view at source ↗

**Figure 8.** Figure 8: Survey results analysis. focused on enumeration, whereas our model extends this by incorporating broader contextual awareness, such as the importance of CVE research. Overall, participants emphasize our model’s ability to combine accurate technical insight with actionable, context-aware strategy compared to the other two models. 6.6 Extended Experiments To further evaluate the Pen-Strategist, we conduct e… view at source ↗

**Figure 9.** Figure 9: Evaluation of different training approaches for strat view at source ↗

**Figure 11.** Figure 11: User prompt used for the Strategy model finetun view at source ↗

**Figure 10.** Figure 10: System prompt used for the Strategy model fine view at source ↗

**Figure 12.** Figure 12: Reward model prompt used to evaluate the derived view at source ↗

**Figure 13.** Figure 13: A sample scenario given in the survey. Here Option 1,2, and 3 are generated using our strategy model, Claude-4.6- view at source ↗

read the original abstract

Cyber threats are rapidly increasing, expanding their impact from large-scale enterprises to government services and individual users, making robust security systems increasingly essential. However, a significant shortage of skilled cybersecurity professionals exacerbates this challenge. While recent research has explored automating tasks such as penetration testing using LLM-based agents, existing frameworks often perform poorly due to limited capability in strategy formulation, domain-specific reasoning, and accurate action and tool selection. To overcome these limitations, we propose Pen-Strategist framework, consisting of a novel domain-specific reasoning model that derives pentesting strategies via logical reasoning and a classifier that converts the strategies into actionable steps. First, we construct a reasoning dataset containing logical explanations for both strategy derivation and step selection in pentesting scenarios. We then fine-tune a Qwen-3-14B model for strategy generation using reinforcement learning. Evaluation on the test split of the dataset demonstrates a 87% improvement in strategy derivation performance compared to the baseline. Furthermore, we integrate the fine-tuned Pen-Strategist model into existing automated pentesting frameworks, such as PentestGPT, and evaluate its performance on vulnerable machines, achieving a 47.5% improvement in subtask completion while surpassing the baseline GPT-5. Further experiments on the CTFKnow benchmark show an 18% performance gain over the base model. For step prediction, we train a semantic-based CNN classifier, which outperforms commercial LLMs by 28% and enhances execution stability. Finally, we conduct a user study to qualitatively assess the generated strategies, and Pen-Strategist demonstrates superior performance compared to the Claude-4.6-Sonnet.

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.

Desk Editor's Note private letter to a colleague

Pen-Strategist adds an RL-fine-tuned Qwen model for pentesting strategy reasoning plus a CNN step classifier, with reported gains on their internal data and some integrations, but the numbers rest on an unreleased custom dataset and undefined baselines.

read the letter

The core contribution is a domain-specific reasoning model fine-tuned with reinforcement learning on a custom pentesting dataset, paired with a separate CNN classifier that turns strategies into executable steps. They integrate the result into PentestGPT and test on vulnerable machines plus the CTFKnow benchmark. That combination is new enough in the LLM-agent pentesting literature to be worth noting, and the attempt to move beyond generic prompting toward explicit logical reasoning is a reasonable direction given how poorly current agents handle strategy formulation. The user study against Claude also gives a qualitative signal that the outputs look better to people who do this work. The paper does a service by releasing the idea of a dedicated reasoning dataset with logical explanations, even if the details are thin in the abstract. The main soft spots are straightforward. All the headline numbers—87% strategy improvement on the test split, 47.5% subtask gain on integration, 18% on CTFKnow, 28% edge for the CNN—come from an internal dataset whose construction, validation, and train/test split are not described. No baseline definitions, metric formulas, statistical tests, or ablation results appear. The vulnerable machines used for integration testing are specific and not shown to represent the broader distribution of real targets. Without the data or code, it is impossible to tell whether the gains are robust or tied to choices that favor the model. The circularity risk is real here: performance is measured on data the authors built, and the integration tests use a small set of machines whose selection criteria are not justified. This is the kind of applied work that belongs in a security or AI-for-security venue. Readers who build or evaluate automated pentesting agents would find the architecture and integration experiments useful as a starting point, provided they can reproduce or extend the dataset. The paper is coherent on its own terms and engages the relevant prior work on LLM agents, so it deserves a serious referee rather than a desk reject. I would send it for review with explicit requests for dataset release, baseline details, and clearer evaluation protocols.

Referee Report

3 major / 2 minor

Summary. The paper proposes Pen-Strategist, a framework with a Qwen-3-14B model fine-tuned via reinforcement learning on a custom-constructed reasoning dataset to derive pentesting strategies through logical reasoning, paired with a semantic CNN classifier to map strategies to actionable steps. It reports an 87% improvement in strategy derivation on the dataset test split versus baseline, a 47.5% gain in subtask completion (surpassing GPT-5) when integrated into PentestGPT and tested on vulnerable machines, an 18% gain on CTFKnow, a 28% gain in step prediction versus commercial LLMs, and superior qualitative results versus Claude-4.6-Sonnet in a user study.

Significance. If the quantitative claims hold under rigorous scrutiny, the work could meaningfully advance LLM-based automation of penetration testing by addressing gaps in strategy formulation and tool selection, with potential to mitigate the cybersecurity skills shortage. The integration experiments with PentestGPT and evaluation on CTFKnow provide partial external grounding beyond the authors' dataset, and the user study adds qualitative support; however, the absence of public data or code release limits immediate reproducibility and broader adoption.

major comments (3)

[Abstract] Abstract: The headline claims of 87% improvement in strategy derivation, 47.5% in subtask completion, 18% on CTFKnow, and 28% in step prediction are presented without defining the underlying metrics (e.g., exact formula for 'strategy derivation performance' or 'subtask completion'), baseline configurations, statistical tests, error bars, or ablation results. This prevents verification of the numbers and raises the possibility of post-hoc metric selection or dataset-specific gaming.
[Dataset construction and evaluation] Dataset and evaluation sections: The central results depend on a privately constructed reasoning dataset whose construction process (generation and validation of logical explanations), train/test split criteria, and leakage controls are not described. Without these details or public release, it is impossible to assess whether the 87% gain on the held-out split reflects genuine generalization or overfitting to the authors' data-construction choices.
[Integration and evaluation] Integration experiments: The 47.5% subtask-completion gain is measured on an unspecified set of 'vulnerable machines' whose selection criteria and representativeness of real-world pentesting targets are not justified. This makes it difficult to determine whether the improvement when plugged into PentestGPT would hold on a broader distribution of targets.

minor comments (2)

[Abstract and Methods] The abstract and results sections would benefit from explicit statements of the reward function and RL hyperparameters used in fine-tuning, as these are free parameters that could influence the reported gains.
[Figures and tables] Figure and table captions should include the exact definitions of all plotted metrics and the number of runs or seeds used to compute averages.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. The comments highlight important areas for improving clarity, transparency, and reproducibility, and we address each major comment point by point below, indicating the specific revisions we will make in the next version of the paper.

read point-by-point responses

Referee: [Abstract] Abstract: The headline claims of 87% improvement in strategy derivation, 47.5% in subtask completion, 18% on CTFKnow, and 28% in step prediction are presented without defining the underlying metrics (e.g., exact formula for 'strategy derivation performance' or 'subtask completion'), baseline configurations, statistical tests, error bars, or ablation results. This prevents verification of the numbers and raises the possibility of post-hoc metric selection or dataset-specific gaming.

Authors: We agree that the abstract would benefit from greater precision to make the claims more verifiable. Strategy derivation performance is measured as the accuracy of generated logical reasoning chains against expert-annotated ground truth on the test split. Subtask completion is the fraction of required subtasks successfully executed by the integrated agent on the target machines. In the revised manuscript we will update the abstract with concise definitions of all reported metrics and add explicit references to the evaluation sections. We will also expand the main evaluation section to include baseline configurations, error bars on all quantitative results, statistical significance tests (paired t-tests with p-values), and ablation studies. These additions will directly address concerns about metric selection and strengthen the evidential basis for the reported gains. revision: yes
Referee: [Dataset construction and evaluation] Dataset and evaluation sections: The central results depend on a privately constructed reasoning dataset whose construction process (generation and validation of logical explanations), train/test split criteria, and leakage controls are not described. Without these details or public release, it is impossible to assess whether the 87% gain on the held-out split reflects genuine generalization or overfitting to the authors' data-construction choices.

Authors: We acknowledge that the dataset construction description in the current manuscript is too concise. In the revision we will substantially expand Section 3 to detail the full pipeline: the expert-crafted and LLM-assisted prompts used to generate logical explanations for both strategy derivation and step selection; the multi-expert validation process together with inter-annotator agreement statistics; the train/test split procedure (an 80/20 split performed at the level of distinct pentesting scenarios with explicit checks to ensure no shared vulnerabilities, tools, or reasoning patterns); and leakage-prevention measures such as scenario diversity sampling and manual overlap audits. While we cannot release the complete dataset because it incorporates proprietary pentesting knowledge, we will include a detailed appendix with representative examples, the full generation methodology, and sanitized sample data to enable independent reproduction of the approach. revision: partial
Referee: [Integration and evaluation] Integration experiments: The 47.5% subtask-completion gain is measured on an unspecified set of 'vulnerable machines' whose selection criteria and representativeness of real-world pentesting targets are not justified. This makes it difficult to determine whether the improvement when plugged into PentestGPT would hold on a broader distribution of targets.

Authors: We agree that additional justification is required. The machines were deliberately chosen from publicly documented vulnerable environments (Metasploitable 2/3, DVWA, and standard CTF challenges) to cover a representative distribution of common vulnerability classes including web application flaws, network misconfigurations, and privilege-escalation vectors. In the revised manuscript we will add a new subsection and accompanying table that lists each machine, its primary vulnerabilities, the rationale for inclusion, and a brief discussion of how the set reflects real-world pentesting targets. This will clarify the scope of the 47.5% improvement and support claims of practical relevance. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical evaluations on held-out splits and external benchmarks remain independent of inputs

full rationale

The paper constructs a custom reasoning dataset, fine-tunes Qwen-3-14B via RL for strategy generation, and reports an 87% improvement on the dataset's test split plus 47.5% subtask gains when integrated into PentestGPT on vulnerable machines and CTFKnow. These are standard empirical measurements against baselines on held-out data and external targets; the reported numbers are not equivalent to the dataset construction or fine-tuning inputs by definition. No self-citations, uniqueness theorems, ansatzes, or renamings reduce the central claims to tautologies. The derivation chain is self-contained with independent validation steps.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claims rest on the assumption that the authors' custom reasoning dataset faithfully encodes pentesting logic and that fine-tuning on it produces generalizable strategies. No explicit free parameters beyond standard RL hyperparameters are named; no new physical or mathematical entities are postulated.

free parameters (1)

RL fine-tuning hyperparameters and reward function
Chosen to optimize strategy generation on the custom dataset; exact values and reward design not stated in abstract.

axioms (1)

domain assumption The constructed reasoning dataset accurately captures logical explanations for pentesting strategy derivation and step selection
Invoked when training and evaluating the model on dataset splits.

pith-pipeline@v0.9.0 · 5604 in / 1364 out tokens · 67950 ms · 2026-05-08T17:44:12.516570+00:00 · methodology

discussion (0)

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Relation between the paper passage and the cited Recognition theorem.

We then fine-tune a Qwen-3-14B model for strategy generation using reinforcement learning... GRPO... reward functions: semantic similarity, pattern, generation length, language reward.

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Reference graph

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testing 123!

Hao Zhang, Tingmin Wu, Tianqing Zhu, Sheng Wen, and Yang Xiang. 2025. CyberLlama: A fine-tuned large language model for cybersecurity named entity recognition.Knowledge-Based Systems(2025), 114183. Conference acronym ’XX, June 03–05, 2018, Woodstock, NY Trovato et al. A Open Science We published our dataset and codes in an anonymous GitHub repos- itory (h...

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[41]

We have sudo privileges to run commands as scriptmanager (no password required)
[42]

We can modify test.py (owned by scriptmanager with rw permissions)
[43]

test.py appears to be executed automatically by root (evidenced by root-owned output) The strategy should be to:
[44]

First, switch to scriptmanager user using our sudo privileges
[45]

Modify test.py to include a reverse shell or privilege escalation payload
[46]

Wait for the automated execution (likely a cron job) to run the script as root
[47]

Exploit this by:

Gain root access Final strategy: 1.4.4 Exploit automated script execution for privilege escalation Since test.py is owned by scriptmanager but creates files owned by root, this indicates the script is being executed by root automatically (likely via cron). Exploit this by:
[48]

Switch to scriptmanager user: `sudo -u scriptmanager /bin/bash`
[49]

Modify test.py to include a reverse shell payload that will execute as root
[50]

Set up a new netcat listener on the attacking machine (different port, e.g., 5555)
[51]

Replace test.py content with a Python reverse shell connecting to attacker IP
[52]

Wait for the automated execution (monitor for incoming connection) Strategy: Exploit potential vulnerabilities within the `test.py` script or use it as a stepping stone to escalate privileges from the compromised `www-data` user. Steps:
[53]

Analyze `test.py`: - Inspect the code of `test.py` to identify if it contains any insecure functionalities, such as hardcoded credentials, misconfigured logging, or unintended side effects when executed with elevated privileges
[54]

Check for SUID/SGID Bits: - Verify if any files in `/scripts` have the setuid/setgid bits enabled, which could allow execution with higher privileges
[55]

Test Execution of `test.py`: - Execute `test.py` under different contexts (e.g., as `root`, via cron jobs, or through web access) to observe its behavior and check if it leads to privilege escalation
[56]

Look for Path Manipulation or Environment Variables: - Search for environment variables that might be manipulated to execute arbitrary code when `test.py` runs
[57]

Consider Web Context: - If the target has exposed web endpoints related to `/scripts`, attempt to trigger `test.py` via a crafted request to see if it behaves differently than locally
[58]

Escalate Privileges via Sudo Rights: - Since `www-data` has sudo rights, focus on escalating from there first before moving into other users
[59]

The previous reconnaissance and privilege escalation steps provided key insights

Review System Logs: - Look for clues in system logs (if accessible) regarding failed login attempts or processes that might indicate misconfigurations. The previous reconnaissance and privilege escalation steps provided key insights. We have accessed the 'scripts' directory intended for the 'scriptmanager' user, revealing files with varied permissions. 't...
[60]

Verifying execution contexts or triggering conditions
[61]

Preparing a backdoor or alternative command within 'test.py' to escalate privileges or explore further
[62]

Managing access to quickly test modifications in 'test.py'
[63]

Focus on gaining 'scriptmanager' privileges to increase system control, permitting further system exploration or access

Checking periodic execution schedules (cron jobs) that might allow leveraging the modified script. Focus on gaining 'scriptmanager' privileges to increase system control, permitting further system exploration or access. Final strategy: Modify 'test.py' within /scripts to include a command that provides a backdoor (e.g., spawn a reverse shell) or logs 'scr...