Autonomous Penetration Testing Frameworks

• Autonomous penetration testing frameworks are automated systems that simulate adversarial attacks using hierarchical models to quantify and assess network vulnerabilities.
• They employ integrated pipelines—data collection, model construction, attack planning, and execution—to provide repeatable, objective security assessments in enterprise and cloud environments.
• Adaptive algorithms, including deep reinforcement learning and Bayesian inference, enhance risk evaluation precision and enable targeted, efficient remediation strategies.

Autonomous penetration testing frameworks are automated software systems designed to perform security assessments of networked systems, emulating adversarial actions with minimal or no human intervention. These frameworks combine advances in attack graph modeling, simulation, multi-agent systems, deep reinforcement learning, and LLMs to automate the generation, planning, and execution of multi-stage cyberattacks. This article presents a rigorous technical overview of the fundamental principles, representative architectures, core methodologies, experimental results, and the main implications for risk assessment and security practice, drawing on representative research with an emphasis on technical fidelity.

1. Foundations and Security Modeling

Autonomous penetration testing frameworks are underpinned by expressive security models and algorithms that systematically enumerate and assess potential attack scenarios. Central in this domain is the use of hierarchical and graphical models, such as the Hierarchical Attack Representation Model (HARM) introduced by HARMer (Enoch et al., 2020). HARM separates global reachability (attack graphs) from local vulnerability detail (attack trees), yielding a scalable, two-layer abstraction. Letting $AP$ denote the set of attack paths from an initial point to a target, path-based metrics such as the minimal path length (shortest attack path $SP$) are defined as:

$SP = \min \left\{ |AP| \right\}$

Risk quantification per path incorporates the product of exploitability probabilities and impact scores:

$Risk_{ap} = \sum_{h \in ap} (p_h \times aim_h)$

where $p_h$ is the likelihood of successful exploitation for host $h$ and $aim_h$ the associated impact measure. These models enable automated attack planning via deterministic algorithms (shortest-path, composite risk, atomic cost) that guide exploit execution in complex, multi-host networks.

2. Autonomous Testing Pipelines: Phases and Architectures

These frameworks typically implement automated pipelines with distinct, tightly integrated phases:

1. Data Collection: Automated gathering of host/network information, vulnerabilities (via Nmap, Nessus, OpenVAS), and contextual threat intelligence (e.g., from MITRE ATT&CK).
2. Security Model Construction: Construction of the global security model (HARM or similar), enabling enumeration of attack surfaces and preconditions.
3. Automated Attack Planning: Deterministic or machine-learned strategies to select attack paths, using quantitative metrics (shortest path, maximum risk, success probability).
4. Automated Attack Execution: Translation of abstract plans into concrete tool-driven actions (Metasploit, Python/Pymetasploit3 wrappers), including feedback and re-planning capabilities upon failure.

By automating model construction and plan execution, such frameworks remove the idiosyncratic dependence on red team practitioners, providing repeatable security analysis workflows. Experimental results in diverse settings (enterprise network segment with 7 hosts, AWS with 100 hosts) demonstrate that frameworks like HARMer can complete data discovery and planning in minutes to hours, with automated multi-step attacks executed in seconds (Enoch et al., 2020).

3. Security Metrics, Algorithms, and Adaptive Testing

The quantification and ranking of attack scenarios in these frameworks is achieved using path-based, composite, and atomic metrics. Attack planning algorithms employ:

• Path-Based Metrics: Prioritize the shortest or lowest-cost attack route.
• Composite Probability: Select attack paths maximizing the probability of cumulative success, e.g., $p_{ap} = \prod_{h \in ap} p_h$.
• Atomic/Incremental Strategy: In environments with partial knowledge, select local actions minimizing attack cost, informed by vulnerability exploitability scores and incremental graph traversal.

Adaptive frameworks such as Autosploit augment classical planning with environmental diagnosis via binary group testing and Bayesian inference (Moscovich et al., 2020), efficiently exploring a combinatorial explosion of environmental parameters. In the presence of noisy test feedback, Bayesian diagnosis is applied:

$$P(d \mid OBS) = \frac{P(OBS \mid d) \cdot P(d)}{P(OBS)}$$

where $d$ is a hypothesis on necessary environmental conditions, and $OBS$ encodes test results. This process enables the automated discovery of which combinations of environmental parameters (e.g., installed packages, file permissions) are required for exploitation, providing precision beyond traditional CVSS-based assessments.

4. Experimental Evaluation and Empirical Results

Autonomous penetration testing frameworks have been validated both in simulated and real-world scenarios, showing demonstrable feasibility and effectiveness. For example:

• HARMer — On a real, multi-segment university network, six attack paths to a high-value host were automatically enumerated and ranked by shortest path and risk, with overall attack impact (AIM) and return on attack (ROA) metrics fully computed post-execution. On AWS cloud topologies (3-tier and flat 100-node topologies), HARMer completed data collection in $\sim$2 hours and attack planning in $<$1s per path (Enoch et al., 2020).
• Autosploit — Using real vulnerabilities (e.g., CVE-2007-2447, CVE-2010-2075), Autosploit’s group-testing algorithms identified precise environmental preconditions for exploitation, achieving high recall and precision in both noiseless and noisy settings (Moscovich et al., 2020).
• Comparative Performance — These frameworks reliably automate multi-step attacks and offer detailed security posture reports, but their completeness and accuracy are inherently capped by the breadth of known vulnerabilities and their ability to recognize complex, adaptive adversary logic.

The use of composable metrics (AIM, ROA, NAS) and rigorous time measurements offer a granular characterization of framework performance and resource utilization.

5. Implications for Automated Security Assessment

By integrating quantitative planning with automated execution, these frameworks enable continuous, objective, and repeatable security assessments. Administrators can use the framework’s metrics—such as the number of attack scenarios (NAS), attack impact (AIM), and return on attack (ROA)—to systematically identify and prioritize critical vulnerabilities. This enables:

• Continuous Testing: Scheduled, repeatable testing of evolving systems, reducing reliance on intermittent and resource-intensive manual red teaming.
• Risk Quantification: More accurate risk metrics than standard vulnerability scores; only vulnerabilities present in exploitable configurations influence assessment, thereby reducing false positives (Moscovich et al., 2020).
• Targeted Remediation: Insights into specific environmental conditions that gate exploitation allow selective hardening and more efficient allocation of security resources.

However, limitations remain: these frameworks generally focus on known, tool-compatible vulnerabilities and may not capture zero-day or highly novel threats. Attack strategies are often deterministic, with adaptive or collaborative attack models requiring further development.

6. Limitations, Challenges, and Future Directions

Current frameworks are typically constrained by several factors:

• Coverage Limits: Dependence on known exploits (e.g., Metasploit modules with CVE mappings) limits assessment to understood vulnerabilities.
• Environmental Complexity: Accurate modeling of real-world, dynamic cloud and hybrid networks—especially with complex access controls and evolving topologies—remains challenging for model construction and scalability.
• Adversary Modeling: While deterministic planning is computable and interpretable, adversaries in reality may collaborate, adapt, or execute multi-phase, low-and-slow campaigns not captured by simple metrics-based planning strategies.
• Automation of Defense: While attack execution is automated, integration with automated defense, countermeasure recommendation, and response mechanisms is not typically implemented in these systems.
• Validation: Empirical validation is often limited to enterprise or academic testbeds; broader standardization of benchmarks and datasets, as well as integration with production-scale infrastructure, is required.

Future research directions include the expansion of adversarial modeling to include adaptive and multi-agent threat scenarios, improved integration of threat intelligence and unknown (zero-day) vulnerability detection, and coupling of automated penetration testing with active defense and remediation workflows. The extension of these frameworks to support defense-in-depth assessments and to handle increasingly dynamic and heterogeneous cloud environments will remain a critical area of work.

7. Summary Table: Key Metrics in HARMer

Metric	Formula or Definition	Role in Framework
Shortest Path (SP)	$\min \{ |AP| \}$	Chooses minimal-length attack paths
Path Risk	$\sum_{h \in ap} (p_h \times aim_h)$	Ranks paths by cumulative risk
Composite Success	$p_{ap} = \prod_{h \in ap} p_h$	Maximizes cumulative success chance
Attack Impact (AIM)	Sum of $aim_h$ across attack path	Measures total post-exploit impact
Return on Attack (ROA)	Ratio of impact to attack cost	Evaluates efficiency of attacks
Number Attack Scenarios (NAS)	Count of paths to target	Measures network's attack exposure

These metrics form the operational, quantitative heart of modern autonomous penetration testing frameworks, supporting automated, repeatable, and scalable security analysis across a broad spectrum of network environments.