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Security Risks of Agentic Vehicles: A Systematic Analysis of Cognitive and Cross-Layer Threats

Ali Eslami, Jiangbo Yu

arXiv:2512.17041v1cs.AIcs.SYeess.SY

TL;DR

Agentic reasoning and upstream vehicle-layer attacks create risks that existing frameworks do not systematically analyze in safety-critical vehicles. The paper maps agentic threats onto a role-based architecture and examines severity and attack chains. It reports that autonomous operation can amplify threats and that cross-role disturbances can cascade into validated but misaligned behavior.

  • Problem

    Existing frameworks do not systematically analyze how agentic reasoning and cross-layer attacks interact in safety-critical vehicles and propagate into driving behavior.

    Source: §1.1 · p. 1

  • Method

    The paper contextualizes agentic risks in a role-based vehicle architecture and analyzes threat severity, cross-role attack chains, and implementation limitations.

    Source: §3.1 · p. 5, §3.2.4 · p. 7, §8 · p. 19

  • Results

    Threats can move from low or medium severity in manual settings to high or critical in autonomous settings, while cross-role disturbances can cascade into validated but misaligned behavior.

    Source: §4.2 · p. 12, §4.3 · p. 14

  • Takeaways & Limitations

    The role-based taxonomy provides a conceptual basis for comparing agentic-vehicle threats and integrating more implementation-level considerations in future research.

    Source: §6.2 · p. 17

    The taxonomy assumes separable Personal and Driving Strategy Agents and does not model emergent vulnerabilities from iterative feedback or jointly adapting internal states.

    Source: §6.2 · p. 17

Abstract

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Agentic AI is increasingly being explored and introduced in both manually driven and autonomous vehicles, leading to the notion of Agentic Vehicles (AgVs), with capabilities such as memory-based personalization, goal interpretation, strategic reasoning, and tool-mediated assistance. While frameworks such as the OWASP Agentic AI Security Risks highlight vulnerabilities in reasoning-driven AI systems, they are not designed for safety-critical cyber-physical platforms such as vehicles, nor do they account for interactions with other layers such as perception, communication, and control layers. This paper investigates security threats in AgVs, including OWASP-style risks and cyber-attacks from other layers affecting the agentic layer. By introducing a role-based architecture for agentic vehicles, consisting of a Personal Agent and a Driving Strategy Agent, we will investigate vulnerabilities in both agentic AI layer and cross-layer risks, including risks originating from upstream layers (e.g., perception layer, control layer, etc.). A severity matrix and attack-chain analysis illustrate how small distortions can escalate into misaligned or unsafe behavior in both human-driven and autonomous vehicles. The resulting framework provides the first structured foundation for analyzing security risks of agentic AI in both current and emerging vehicle platforms.

1 Introduction

Agentic reasoning introduces cognitive and cross-layer attack paths that existing vehicle-security frameworks do not systematically address. This paper proposes a role-based analysis and maps threats, severity, and attack chains across vehicle layers.

1.1 Motivation

  • Small distortions in intent or stored context can reshape vehicle behavior without standard detectors, and upstream attacks can mislead agents even when they are uncompromised.The paper identifies cross-role propagation as a route from subtle cognitive or upstream disturbances to misaligned or unsafe behavior.Source: §1.1 · p. 1

1.2 Gap in Existing Literature

  • Existing automotive and AI-security frameworks leave a gap in analyzing agentic reasoning within safety-critical vehicle architectures and its propagation into driving behavior.The paper notes that automotive standards focus on communication and hardware, while AI threat models generally target non-vehicle applications.Source: §1.1 · p. 1, §1.2 · p. 2

1.3 Contributions

  • The paper decomposes the agentic layer into Personal and Driving Strategy Agents, classifies cognitive threats, assesses their effects, and analyzes attack chains and cross-layer manipulation.Its contributions include a role-based architecture, a threat taxonomy, severity assessment, and pathways from perception, communication, and control attacks into agentic reasoning.Source: §1.3 · p. 2

1.4 Paper Organization

  • The paper proceeds from prior work to the architecture, threat analysis, cross-layer risks, implications, case studies, and conclusion.The introduction identifies Sections 2 through 8 as covering these topics.Source: §1.4 · pp. 2–3

2 Background & Related Works

Vehicle security risks span physical sensing, internal networks, wireless communication, and cloud services, while agentic capabilities introduce additional cognitive attack surfaces. Existing automotive and agentic-AI frameworks address these domains separately, motivating a vehicle-specific account of their interaction.

2.1 Cybersecurity risks in Connected and Autonomous Vehicles: Layered Analysis

  • Perception, control and network, V2X, and cloud/application layers expose distinct attack paths that can distort environmental understanding, coordination, or vehicle behavior.Examples include manipulated sensor inputs, forged CAN messages, fabricated V2X warnings, and compromised map or update services.Source: §2.1 · p. 3

2.2 Agentic AI and Levels of Agency

  • The six agency levels range from reactive rule-based tools to general agents with dynamic goal-setting, broad tool use, and reflective plan modification.The levels are orthogonal to SAE driving-automation levels; a vehicle may host agents at any agency level.Source: §2.2 · p. 4

2.3 Existing Threat Models and OWASP Agentic AI Risks

  • The paper positions its analysis at the intersection of automotive cybersecurity and reasoning-driven, memory-based, goal-oriented agent behavior.Existing CAV work primarily addresses network security, communication authenticity, and physical-layer safety mechanisms.Source: §2.3 · p. 4

2.3.1 Traditional Automotive Security Standards

  • Traditional automotive frameworks address hardware, networks, and communications but assume deterministic components, leaving reasoning-driven failures and agentic cognition outside their threat models.The paper contrasts the coverage of standards such as ISO/SAE 21434 and UNECE WP.29 with vulnerabilities involving memory, goals, and reasoning.Source: §2.3.1 · p. 4, §2.3.3 · p. 5

2.3.2 OWASP Agentic AI Security Risks

  • OWASP agentic risks cover threats such as memory poisoning, cascading reasoning errors, privilege misuse, and unsafe tool invocation, but are domain-agnostic and omit vehicle safety constraints.The taxonomy does not capture how these risks manifest when agent outputs influence driving decisions.Source: §2.3.2 · pp. 4–5

2.3.3 Gap in Existing Frameworks and Position of This Work

  • The paper addresses the gap by contextualizing fifteen OWASP agentic risks within a role-based vehicle architecture and examining interactions with perception, V2X, fusion, and control layers.The analysis considers how distorted intent, corrupted inter-agent messages, and upstream CAV-layer attacks can propagate through vehicle reasoning.Source: §2.3.3 · p. 5

3 Agentic Vehicle Architecture

The proposed architecture partitions vehicle reasoning into bounded roles and keeps final safety verification deterministic. Its authority and trust boundaries constrain how agent outputs influence the driving pipeline, including in optional future extensions.

3.1 Role-Based Multi-Agent Design Principles

  • Role-based design distributes intent interpretation, behavior generation, and safety validation across mediated, auditable agents rather than granting one agent all three functions.Authority boundaries limit actions and outputs, while trust boundaries govern relied-upon inputs and treatment of messages; these boundaries limit damage from compromise or misalignment.Source: §3.1 · p. 5

3.2.1 Driving Strategy Agent (DSA)

  • The Driving Strategy Agent integrates intent, environmental summaries, traffic, and regulations to propose candidate maneuvers, but does not command actuators.Its structured recommendations go to the Safety Check Layer for downstream validation.Source: §3.2.1 · p. 6

3.2.2 Personal Agent (PA)

  • The Personal Agent maintains user preferences and long-term context, converting dialogue and external information into abstract intent descriptors rather than maneuver proposals.It may query services such as navigation, map, traffic, and charging infrastructure providers.Source: §3.2.2 · p. 6

3.2.3 Deterministic Safety Check Layer (Non-Agentic)

  • A stateless, rule-based Safety Check applies predefined physical, regulatory, and operational constraints to DSA proposals before the deterministic control stack.Its checks include collision envelopes, acceleration and jerk limits, road geometry, and regulatory compliance.Source: §3.2.3 · p. 6

3.2.4 Optional Agentic Extensions, Sub-Agents, and Orchestration Considerations

  • Future health-monitoring, infrastructure/cooperation, and semantic-interpretation sub-agents may operate under the DSA or PA without expanding core decision authority.The proposed architecture treats most additions as sub-agents within the two primary roles.Source: §3.2.4 · pp. 6–7
  • The design avoids standalone high-privilege orchestrator and safety-monitor agents, using PA–DSA pathways for coordination and deterministic checks for final verification.Reasoning-based safety monitoring is left for future research; the paper focuses on risks and attack pathways rather than defense or detection mechanisms.Source: §3.2.4 · p. 7

3.3 Inter-Role Communication and Dependencies

The communication pipeline passes abstract user intent to the Driving Strategy Agent, whose proposals receive safety validation before affecting control. The architecture diagram depicts these role boundaries and the constrained flow between agentic and CAV layers.

3.3 Inter-Role Communication and Dependencies

  • The Personal Agent frames goals and preferences, the Driving Strategy Agent develops policy options, and the Safety Check approves, revises, or replaces proposals before control influence.This separation prevents any one role from interpreting intent, generating a maneuver, and authorizing its execution.Source: §3.3 · p. 7

3.4 Architecture Diagram

  • The diagram shows perception, fusion, V2X, and cloud information flowing into the agentic roles, while proposals influence planning only after Safety Check validation.A trust boundary separates the agentic layer from safety-critical subsystems, with no direct agent path to actuators.Source: §3.4 · pp. 7, 9

4.1 Threat Analysis

The section interprets fifteen agentic security risks through the Personal Agent (PA) and Driving Strategy Agent (DSA), tracing how manipulated inputs or reasoning can shape planning while proposals still appear valid to Safety Checks (SC).

4.1 Threat Analysis

  • The role-based analysis explains how each of fifteen threats can affect agent interactions and propagate cognitive disturbances into vehicle decision pipelines.It focuses on safety-relevant pathways in the Agentic Vehicle context.Source: §4.1 · p. 9

4.1.1 T1: Memory Poisoning; 4.1.5 T5: Cascading Hallucinations

  • Memory poisoning can distort stored preferences or cues, while cascading hallucinations let false context compound through PA-to-DSA planning and persist across sessions.The resulting strategies may remain coherent and pass SC review despite corrupted assumptions.Source: §4.1.1 · p. 9, §4.1.5 · p. 10

4.1.2 T2: Tool Misuse; 4.1.3 T3: Privilege Compromise; 4.1.11 T11: Unexpected Remote Code Execution

  • Tool misuse can steer permitted tool calls, privilege compromise can grant unintended authority, and vulnerable tool chains can execute unauthorized code that influences planning.Manipulated outputs or instructions may appear procedurally valid or legitimate, allowing proposals to pass SC review.Source: §4.1.2 · p. 9, §4.1.3 · p. 9, §4.1.11 · p. 11

4.1.4 T4: Resource Overload; 4.1.10 T10: Overwhelming the Human-in-the-Loop; 4.1.14 T14: Human Attacks on Multi-Agent Systems; 4.1.15 T15: Human Manipulation

  • Resource overload degrades agent processing; human overload, deliberate user attacks, and human-facing manipulation can also distort feedback that the DSA uses for planning.The user may provide rushed, inconsistent, noisy, or agent-influenced input that is treated as reliable context.Source: §4.1.4 · p. 9, §4.1.10 · p. 11, §4.1.14 · p. 11

4.1.6 T6: Intent Breaking; 4.1.7 T7: Misaligned and Deceptive Behaviors

  • Intent breaking shifts the PA’s representation of user goals, while misaligned behavior skews the criteria the DSA uses to rank plans.Both can yield coherent strategies that remain within physical safety bounds and pass SC review.Source: §4.1.6 · p. 10, §4.1.7 · p. 10

4.1.8 T8: Repudiation and Untraceability; 4.1.9 T9: Identity Spoofing; 4.1.12 T12: Agent Communication Poisoning

  • Repudiation weakens attribution, identity spoofing forges trusted sources, and communication poisoning injects false inter-agent messages that can shape DSA planning.These attacks exploit missing provenance or inputs accepted as legitimate, while the resulting plans may look structurally sound.Source: §4.1.8 · p. 10, §4.1.9 · p. 10, §4.1.12 · p. 11

4.1.13 T13: Rogue Agents

  • A rogue PA, DSA, or tool helper can gradually act outside its intended role and covertly steer planning toward adversarial objectives.Outputs may stay within safe limits, allowing the influence to persist without obvious alarms.Source: §4.1.13 · p. 11

4.2 Threat Severity Analysis

The severity analysis compares threats across manual and autonomous settings and finds that autonomous operation and greater agentic reasoning raise the salience of semantic threats.

4.2 Threat Severity Analysis

  • Threat severity sums four ordinal ratings—Safety Impact, Stealth & Detectability, Persistence, and Semantic Misalignment—across six operating contexts.Ratings map L, M, H, and C to 1, 2, 3, and 4; totals are grouped into four severity bands.Source: §4.2 · p. 12
  • In manual driving, most threats remain in lower severity bands at low agency, but reasoning and delegation threats rise as agentic capability grows.Human supervision dominates when agentic components have limited influence on driving behavior.Source: §4.2 · p. 12
  • Autonomous operation amplifies threats across agency levels, while semantic threats move toward high and critical severity as agentic reasoning contributes to policy generation.The analysis identifies memory poisoning, cascading hallucinations, intent breaking, misaligned behavior, and rogue agents among those semantic threats.Source: §4.2 · p. 12

4.3 Cross-Role Attack Chains

The attack chains show how small distortions can pass through agent roles and Safety Checks to produce persistent or otherwise misaligned strategies.

4.3 Cross-Role Attack Chains

  • Memory poisoning can shift later intent encoding, leading the DSA to optimize for misaligned goals while its physically safe strategy passes SC review and sustains behavioral drift.The chain links T1 memory poisoning, T6 intent drift, and T7 misaligned strategy.Source: §4.3 · p. 12
  • A forged identity can enable false environmental messages that the DSA treats as authoritative, prompting unnecessary detours or suboptimal maneuvers that SC does not reject.The chain links T9 identity spoofing, T12 communication poisoning, and T7 misaligned strategy.Source: §4.3 · p. 13
  • A hallucinated detail can gain confidence through the DSA’s multi-step reasoning and gradually shape a coherent but flawed policy.This chain connects cascading hallucinations to compounding errors and undetected misalignment.Source: §4.3 · p. 13
  • A perception attack can create a coherent but incorrect scene summary that the PA or DSA accepts, compounding into confident, misaligned strategies approved by SC.The example includes manipulated road signs and spoofed LiDAR returns.Source: §4.3 · p. 13
  • Across these chains, localized cognitive disturbances or upstream corruption cascade through reasoning into validated but misaligned behavior, motivating defenses across roles.The analysis cautions against protecting individual components in isolation.Source: §4.3 · p. 14

5 Integrated Attacks Across CAV Layers and Agentic AI Agents

Agentic reasoning depends on upstream CAV summaries, so attacks on perception, communication, computing, decision, or control can affect agentic behavior without directly manipulating its memory or goals.

5.1 Perception-Layer Attacks

  • Perception attacks can falsify fused scene summaries, shifting DSA maneuver evaluations or PA workload judgments while remaining within SC’s physical and regulatory envelopes.Examples include manipulated signs, altered lane markings, sensor spoofing, and phantom objects.Source: §5.1 · p. 15

5.2 Communication-Layer Manipulation

  • Spoofed V2X, cooperative-perception, or roadside messages can become trusted context, prompting false hazard responses or urgency and potentially persisting in historical preference models.Persistence may occur if manipulated signals are logged or incorporated into those models.Source: §5.2 · pp. 15–16

5.3 Upstream Corruption in the Computing and Decision Layers

  • Compromised computing or decision modules can bias abstracted state summaries even when sensors remain uncompromised, leading PA and DSA behavior to adapt to credible but corrupted context.The described avenues include OTA update tampering, neural-network weight manipulation, or insider modification.Source: §5.3 · p. 16

5.4 Misleading Feedback from the Control Layer

  • Falsified control feedback such as velocity, steering, or braking signals can prompt unnecessary mode changes or degraded driving profiles that SC validates as internally consistent.The PA may also adjust comfort or preference parameters based on the misreported performance.Source: §5.4 · p. 16

5.5 Illustrative Cross-Layer Attack Chains

  • Illustrative chains show phantom objects and spoofed road-closure messages cascading into conservative profiles, rerouting, or misleading user instructions that the agents treat as authoritative.The examples produce validated yet misaligned behavior through upstream manipulation.Source: §5.5 · p. 16

6 Discussion

The design implications pair bounded agent roles and deterministic safety enforcement with semantic monitoring and layered defenses. The taxonomy remains conceptual: it abstracts away role coupling, emergent interactions, and implementation differences, while providing a basis for future implementation-level analysis.

6.1 Implications for Agentic Vehicle Design

  • Role separation and bounded authority isolate failures, while constraining agency can limit risk but may also let attackers degrade performance by lowering agency.The PA interprets intent, the DSA proposes behavior, and safety enforcement remains separate.Source: §6.1 · p. 16
  • Semantic consistency checks for perception and V2X context should complement a deterministic safety gate that enforces physical and rule-based constraints, including when validation is delayed.The safety gate remains the final authority even when semantic validation and multi-source checks introduce latency.Source: §6.1 · pp. 16–17
  • Predictable human-agent interactions can reduce confusion and indirect manipulation, while provenance tracking supports semantic-drift detection and post-incident analysis at computational cost.The discussion links structured clarification, uncertainty, and decision summaries to driver understanding and workload.Source: §6.1 · p. 17
  • A tightly scoped Safety Monitor Agent can check proposal coherence, but its outputs must feed the deterministic layer and failures should trigger safety checks or conservative defaults.Restricted authority and isolated memory and tool-access boundaries limit the SMA's added attack surface.Source: §6.1 · p. 17
  • An orchestrator can govern information flow and provenance, but richer coordination increases latency, complexity, and vulnerability; cross-layer attack chains also call for distributed defenses.The proposed defense examples include invariants, drift detection, provenance checks, and cross-source validation.Source: §6.1 · p. 17

6.2 Limitations of the Role-Based Taxonomy

  • The taxonomy assumes clear PA–DSA separation, although shared resources or co-located execution can blur boundaries and create hidden dependencies it does not capture.The abstraction may therefore miss implementation effects on real-world system behavior.Source: §6.2 · p. 17
  • The taxonomy omits emergent vulnerabilities from iterative feedback or jointly adapting reasoning modules, especially amid long-term memory, multi-agent negotiation, or tool-driven autonomy.Such vulnerabilities may not map directly to a single threat or role.Source: §6.2 · p. 17
  • The framework abstracts model, training, fine-tuning, and verification differences, even though implementation choices can alter the effective attack surface.Despite these limits, the taxonomy offers a conceptual basis for comparing threats and integrating implementation-level considerations in future work.Source: §6.2 · p. 17

7 Case Studies

The case studies show that poisoned memory and corrupted infrastructure context can shift target speeds while remaining within physical safety limits. These examples illustrate how semantic distortions can propagate through agent roles without being flagged by the Safety Check layer.

7.1 Case Study I: Memory Poisoning of the Personal Agent

  • A poisoned long-term memory entry suggesting a roughly 45 km/h ceiling changes how the PA interprets future driver requests and produces modified high-level preferences for the DSA.The study examines eight representative scenarios, including urgent and routine driving; the Safety Check validates physical limits rather than preference distortions.Source: §7.1 · p. 18
  • After memory poisoning, target speeds fall from contextual expectations of 80–90 km/h to 30–60 km/h across scenarios; Scenario 1 drops from 80 to 40 km/h despite unchanged driver intent.The Safety Check approves these physically safe speeds, allowing the semantic distortion to propagate silently.Source: §7.1 · p. 18

7.2 Case Study II: Infrastructure-Induced Policy Misalignment

  • Scenario 0 falls from roughly 90 to 36 km/h, while residential driving falls from 45 to 9 km/h under corrupted infrastructure descriptions; Figure 3 compares before-and-after target speeds.The reported high-speed cases experience the largest degradations, and all four scenarios show lower targets.Source: §7.2 · pp. 18–19, Figure 3 · p. 19
  • Falsified infrastructure or map context can mislead the DSA upstream of the agentic layer, while neither the PA nor the Safety Check detects the semantic corruption.The reduced targets pass the deterministic check because they remain physically safe, producing persistent misaligned behavior without violating physical constraints.Source: §7.2 · pp. 18–19

8 Conclusion

The paper proposes a role-based framework for analyzing agentic-vehicle security and maps OWASP Agentic AI risks across the Personal Agent and Driving Strategy Agent. It concludes that attacks on traditional vehicle layers can mislead agentic modules through corrupted summaries or context, creating pathways that evade conventional cyber-physical defenses.

8 Conclusion

  • The framework maps fifteen OWASP Agentic AI Security Risks across the Personal Agent and Driving Strategy Agent, showing that threats manifest differently across roles.The paper presents the framework as a foundation for analyzing agentic-vehicle risks in systems influencing manually driven and autonomous vehicles.Source: §8 · p. 19
  • Attacks on perception, V2X communication, computing, or control can mislead agentic modules through corrupted summaries or falsified contextual cues, creating integrated pathways that evade traditional cyber-physical defenses.Future work is directed toward defenses, authentication and provenance, Safety Monitor Agent robustness, and experimental evaluation through simulation, attack-chain testing, and prototypes.Source: §8 · p. 19
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