RefPentester Framework: Self-Reflective AutoPT

• RefPentester is a knowledge-informed, self-reflective automated penetration testing framework that integrates retrieval-augmented guidance and a trial-and-error feedback loop.
• It features a modular architecture with dedicated components like Process Navigator, Generator, Reflector, and success/failure logs to ensure refined operational decisions.
• Empirical evaluations demonstrate significant improvements in multi-stage test transitions and credential capture compared to baseline LLM-based approaches.

RefPentester is a knowledge-informed, self-reflective automated penetration testing (AutoPT) framework powered by LLMs. Developed to address critical deficiencies in prior LLM-based AutoPT solutions—specifically imbalanced domain knowledge, shallow planning, hallucinatory command synthesis, and a lack of adaptive learning from failure—it integrates retrieval-augmented contextual guidance, an explicit trial-and-error feedback mechanism, and a formalized seven-stage process model of penetration testing. RefPentester’s architecture is modular, employing dedicated LLM sessions chained across five main components (Process Navigator, Generator, Reflector, Success Log, and Failure Log), each tightly coupled by persistent context and learned operational trajectories. The framework is empirically validated on the Hack The Box Sau target, where it significantly outperforms baseline approaches (e.g., GPT-4o) for both task completion and success on multi-stage transitions (Dai et al., 11 May 2025).

1. Architecture and Core Components

RefPentester’s pipeline is orchestrated by five principal modules:

• Process Navigator: Determines the current stage of the penetration testing (PT) process and retrieves structured, high-level domain knowledge—tactics, techniques, and actions—via a retrieval-augmented generation (RAG) pipeline. This component leverages a vectorized database (VDB) constructed from curated sources such as MITRE ATT&CK and the OWASP Testing Guide; embeddings are generated (e.g., via llama-text-embed-v2-index), and high-dimensional cosine similarity is used for retrieval:

$$\vec{c}_t = \mathop{\arg\max}_{\vec{c} \in \mathbb{V}_{\mathcal{C}}} \text{Cosim}\left(\mathrm{Embed}(q_t \oplus \tau_t), \vec{c}\right)$$

where $q_t$ is the current query and $\tau_t$ is the current PT stage.

• Generator: Produces granular, operational guidance for the PT operator based on high-level context, taking into account preceding failures (via the Failure Log) in its generation.
• Reflector: Implements the self-reflective loop by evaluating each action’s outcome using a formal reward function,

$R(\tau, c, u, a, g, o)$

where $\tau$ is the PT stage, $c$ the tactic, $u$ the technique, $a$ the abstract action, $g$ the generated guidance, and $o$ the observed result. Outcomes are classified as successful ($R=2$), partially correct ($R=1$), or failed ($R=0$), triggering corresponding updates in Success or Failure Logs.

• Success Log / Failure Log: Maintain linear, timestamped records of operations and their results. These logs supply essential short-term memory and enable iterative refinement and adaptation within the framework.

Data flow is orchestrated such that each component’s output, including failures and rationale, propagates forward, ensuring subsequent guiding actions are informed both by structured PT knowledge and the model’s own operational history.

2. Knowledge-Informed Decision Process

A critical innovation is the explicit decoupling of abstract PT tactics (from MITRE/OWASP, etc.) and their instantiation as actionable steps through RAG. The triple-tier knowledge taxonomy—tactics ($\mathcal{C}$), techniques ($\mathcal{U}$), actions ($\mathcal{A}$)—is systematically encoded in the VDB. During operation, the Process Navigator computes cosine similarity over these embedded sets (with thresholding for action selection),

$$\mathbb{V}_{\mathcal{A}} = \{\vec{a}_1, \ldots, \vec{a}_n\}, \quad A_t = \left\{a_t : \text{Cosim}(\mathrm{Embed}(q_t), \vec{a}_t) > \lambda \right\}$$

ensuring the Generator is grounded in contextually valid operational advice. This architecture mitigates hallucinations and unbalanced suggestions inherent to LLMs trained on incomplete or skewed datasets.

3. Self-Reflective Mechanism and Adaptive Iteration

RefPentester is distinctive for its capacity for structured reflection based on real-world action outcomes. After a recommended action is executed on the target and an output $o$ is returned, the Reflector re-invokes the LLM to:

• Compare the intent ($g$) and observed outcome ($o$) using the reward function $R$.
• For partial or failed attempts ($R{=}1,0$), forward failure data and LLM-generated failure rationales to the Failure Log.
• For successes ($R{=}2$), commit operational data to the Success Log.

This feedback informs subsequent Generator invocations, which may adjust their guidance based on accumulated failed attempts, reducing repeated errors and enabling convergence on workable PT strategies. The presence of such a self-corrective loop directly addresses the “trial-and-error” challenge in automated testing, a major limitation of prior LLM-based approaches.

4. Seven-State Stage Machine Model

The PT process is represented as a finite-state machine with seven discrete states:

• $\theta_1$: Information Gathering
• $\theta_2$: Vulnerability Identification
• $\theta_3$: Exploitation
• $\theta_4$: Post-Exploitation
• $\theta_5$: Capture the Flag
• $\theta_6$: Documentation
• $\theta_7$: Terminal Process

Transitions are event-triggered (e.g., from $\theta_1$ to $\theta_2$ on event $\alpha$ = “Gathered Information”, from $\theta_2$ to $\theta_3$ on event $\beta$ = “Identified Vulnerability”), and repeated lack of forward progress (e.g., no transition within a bounded number of iterations) results in termination or fall-back. Each stage effectively delimits a discrete sub-goal, supporting precise targeting of PT guidance, improved context management, and formal completeness guarantees for the automated test cycle.

5. Empirical Evaluation and Quantitative Results

RefPentester’s effectiveness was benchmarked against GPT-4o on the Hack The Box “Sau” environment:

• Success in credential capture (100% for RefPentester vs. 83.3% for baseline).
• Substantial gains in discrete PT stage transitions: e.g.,
  • Information Gathering: 80% vs. 61.5%
  • Vulnerability Identification: 87.5% vs. 35.7%
  • Exploitation, Post-Exploitation, and Capture the Flag likewise showed marked improvement.
• Correct and complete coverage of all required flags for the scenario.

These empirical results demonstrate the benefit of chained knowledge retrieval, structured process modeling, and a feedback-driven operational loop.

6. Technical Details and LaTeX Mechanisms

Key technical constructs emphasized in the pipeline include:

• Embedding-based RAG: High-dimensional vector space search for tactic/technique/action selection.
• Reward function as a formal grammar for self-assessment, generalizing traditional trial-and-error loops from human-guided pentesting.
• Persistent log management, enabling both short-term context propagation (to avoid re-hallucination of failed strategies) and long-term learning.

All LLM-invoked steps and actions explicitly encode stage, context, action rationale, and outcome, supporting ablation, explainability, and reproducibility.

7. Prospects and Future Extensions

RefPentester’s design is extensible:

• Future work includes diversified scenario validation, component ablation studies to optimize architecture, and dynamic VDB updating as attacks and defenses evolve.
• Integration of Reinforcement Learning with Human Feedback (RLHF) is outlined as a trajectory for evolving the reflection mechanism, possibly automating or weighting reward assignment.
• Potential hybridization with existing tools (e.g., Metasploit) and embedding of standards-aligned ethical compliance mechanisms are projected as practical enhancements.

This modular, knowledge-pipelined, self-reflective automation paradigm positions RefPentester as a foundational architecture for future LLM-based penetration testing research and scalable, real-world AutoPT deployments (Dai et al., 11 May 2025).