# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 2 Satellite Networks Penetration Testing Method
### Automation Degree Selection  
From the perspective of automation, penetration testing can be categorized into manual, semi-automatic, and fully automatic approaches, a classification that offers methodological inspiration for security researchers and enumerates corresponding technical options across automation levels.  
Manual penetration testing in satellite networks relies on human expertise to explore vulnerabilities. Security professionals analyze attack vectors by inspecting network traffic anomalies, identifying logic flaws, cryptographic weaknesses in cross-link communications, or misconfigured access controls. While this approach enables detection of mission-critical vulnerabilities, its labor-intensive nature and reliance on domain expertise limit practical applicability in large-scale satellite constellations. Moreover, the escalating complexity and sophistication of cyber-attacks increasingly impede human experts' ability to maintain pace with the evolving threat landscape, thereby compromising the timely formulation of effective countermeasures against adversarial actors [23].  
Semi-automatic penetration testing merges human expertise with automated tools and scripts, enabling testers to harness the efficiency of automation while maintaining critical oversight for decision-making and targeted interventions. Contemporary semi-automatic penetration testing in satellite networks increasingly integrates AI-driven frameworks. Machine learning models trained on historical space cyber incidents dynamically parse telemetry datasets to predict orbital-specific attack vectors. Neural networks optimized for space-qualified protocols enhance real-time anomaly detection. Utilizing advanced LLMs supports penetration testers in improving the efficiency of their workflow [24]. LLMs enhance the efficiency of generating tailored exploit scripts and autonomously debugging vulnerabilities within satellite network systems. Overall, semi-automatic penetration testing strikes a balance between speed and thoroughness, making it a popular choice for comprehensive penetration testing.  
Fully automatic penetration testing leverages autonomous AI agents to execute continuous security assessments, employing reinforcement learning to adapt attack strategies. The collaborative multi-agent system enhances penetration testing efficiency by employing specialized, coordinated agents for reconnaissance, scanning, and exploitation. This modular architecture addresses context loss and data deluge through phased task execution and autonomous decision-making [25]. However, ethical risks persist, as overly aggressive autonomous penetration testing agents might inadvertently disrupt critical services or leak sensitive data during exploitation. Additionally, while AI excels at pattern recognition,  
\begin{table}
\begin{tabular}{c c c} \hline
**Segment** & **Key Testing Methodologies** & **Anticipated Hazards** \\ \hline User Segment & IoT firmware analysis; & \begin{tabular}{c} Insecure credential management; \\ unencrypted user links \\ \end{tabular} \\ \hline Ground Segment & API endpoint audits; & \
\begin{tabular}{c} Unpatched legacy systems; \\ misconfigured access controls \\ \end{tabular} \\ \hline Space Segment & Inter-satellite link encryption validation; &
\begin{tabular}{c} Orbital collision vulnerabilities; \\ on-board software integrity failures \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 2: Correlation of testing methodologies and anticipated hazards by segment.  
it struggles with creative problem-solving in novel attack scenarios, necessitating hybrid human-AI approaches for comprehensive satellite network security.  
Testers can select appropriate automation levels--from manual, expertise-driven methods to autonomous AI agents--based on their resources, skills, and project needs, ensuring effective and efficient penetration testing aligned with operational contexts.