## 3 Methodology
### Attack Vector Analysis from AI Engine Surveys
#### 4.3.5 Comparative Analysis Between AI Agents  
A comprehensive examination of the AI-driven evaluations from ChatGPT, DeepSeek, Copilot, and Gemini reveals notable patterns and divergences in how these engines perceive and assess cybersecurity threats in autonomous vehicles. While all four AI agents largely converge on the notion that network-based attacks represent the most pressing threat, there are significant nuances in their individual assessments. For instance, replay attacks consistently received high severity scores across all engines, ranging from 8.7 to 9.2, underscoring their perceived potential to disrupt system integrity and availability. Similarly, falsified information and cloaking attacks were broadly rated as severe, indicating a shared recognition of the risks posed by data manipulation and evasion tactics. However, substantial variability is evident in the severity scoring of certain attacks. Blinding attacks, for example, were rated as low-severity by DeepSeek (4.4) and Copilot (5.4), but Gemini assessed them much more critically at 8.3, suggesting divergent interpretations of the attack's real-world impact. Disruptive attacks, too, displayed a similar divergence, with ChatGPT and DeepSeek rating them as highly critical (above 8.7), while Copilot considered them significantly less severe (4.4). These discrepancies may reflect differences in how each AI model contextualizes physical attacks or how much weight they assign to environmental versus intrinsic factors. The AI engines also varied in their treatment of attack complexity. ChatGPT and Copilot emphasized the threat of low-complexity attacks, highlighting their accessibility and repeatability, which is crucial in assessing how easily an adversary might exploit a vulnerability without requiring advanced techniques or knowledge. In contrast, Gemini and DeepSeek leaned more heavily toward classifying attacks as high complexity, perhaps signaling a more conservative or risk-averse interpretation based on system-level constraints or defensive assumptions. Another area of divergence lies in privilege requirements. ChatGPT and Copilot consistently emphasized that most attacks demand little to no privileges, thereby broadening \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Attack Type** & **AV** & **AC** & **AT** & **PR** & **UI** & **C** & **I** & **A** & **Avg. CVSS Score** \\ \hline Blinding Attack & Network & High & Present & None & None & None & High & High & 8.3 \\ \hline Jamming Attack & Network & Low & Present & None & None & None & High & High & 8.3 \\ \hline Black Hole Attack & Network & High & Present & Low & None & None & High & High & 6.1 \\ \hline Timing Attack & Network & High & Present & Low & None & High & High & High & _7.7_ \\ \hline Disruptive Attack & Network & High & Present & Low & None & None & High & High & 6.1 \\ \hline Replay Attack & Network & High & Present & None & None & None & High & High & 8.3 \\ \hline Relay Attack & Network & High & Present & None & None & Low & High & High & 8.4 \\ \hline Eavesdropping Attack & Network & Low & Present & None & None & High & None & None & 8.2 \\ \hline Sybil Attack & Network & High & Present & Low & None & High & High & High & _7.7_ \\ \hline Blind Spot Exploitation & Local & Low & Present & None & None & None & High & High & 5.9 \\ \hline Sensor Interference Attack & Network & High & Present & None & None & None & High & High & 8.3 \\ \hline Acoustic Attack & Local & High & Present & None & None & Low & High & High & _6.0_ \\ \hline Impersonation Attack & Network & High & Present & Low & None & High & High & High & _7.7_ \\ \hline Falsified Information Attack & Network & High & Present & Low & None & High & High & High & _7.7_ \\ \hline Cloaking Attacks & Network & High & Present & Low & None & High & High & High & _7.7_ \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Gemini survey result. the potential attacker base and highlighting the need for systemic defenses rather than access control alone.