Agentic Security in Multi-Agent Systems

• Agentic security is the practice of securing autonomous LLM-driven multi-agent systems using formal semantic models and layered controls.
• It employs DAG-based task decomposition to mitigate coordination misalignments and prevent privilege escalation and deadlocks.
• Temporal logic and model checking rigorously verify safety, liveness, and consistency within both orchestration and task lifecycle frameworks.

Agentic security is the discipline and practice of securing autonomous, LLM-driven multi-agent systems and their orchestration protocols. Such systems decompose complex tasks across networks of agents, relying on dynamic inter-agent communication, tool access, and persistent memory. The resulting architectures create new attack surfaces, systemic risks, and emergent vulnerabilities distinct from traditional monolithic or deterministic AI models. Core agentic security challenges include coordination misalignment, privilege escalation across compositional boundaries, prompt-based and logic-layer attacks, memory/context poisoning, and insufficient protocol guarantees preventing policy or workflow violations. Rigorous models, property specifications, and systematic verification frameworks are necessary for reliable, secure agentic deployments—particularly in settings with high-stakes or adversarial demands.

1. Unified Semantic Modeling of Agentic System Security

The foundational approach to agentic security described in (Allegrini et al., 15 Oct 2025) is based on unifying the semantic models governing both macro-level orchestration and micro-level execution. The host agent model $\mathcal{H}$ formalizes the top-level orchestrator’s role as the entity that receives user requests, decomposes them into subtasks, delegates across agents (using A2A for agent-to-agent delegation, MCP for tool access), and aggregates task results. This model encompasses:

• The agent set $\mathcal{A}$
• External entities $\mathcal{E}$, task set $\mathcal{T}$
• Registry $$\mathcal{R}: \mathcal{E} \rightarrow \mathcal{P}(\cdot)$$
• Orchestrator $\mathcal{O}$, host core $\mathcal{C}$, communication layer $\mathcal{C}_L$
• Global state space $\mathcal{S}_\mathcal{H}$

Task decomposition forms a directed acyclic graph (DAG) representing subtasks and dependencies: $$\mathcal{O}_{\mathrm{decomp}}: \mathcal{I}_U \times \mathcal{P}(\mathcal{E}) \rightarrow \mathcal{D}$$ Individual subtask execution is captured by the task lifecycle model $$\mathcal{L} = (\mathcal{S}_t, s_0, \mathcal{E}_t, \delta)$$, which defines enumerated states such as CREATED, IN PROGRESS, COMPLETED, ERROR, and state transition functions $\delta$.

This modeling separation enforces separation of concerns: global coordination and verification are handled at the host agent layer; fine-grained control, state progression, and error/failure management are abstracted into the task lifecycle. This structure mitigates ambiguities at protocol and state boundaries that would otherwise allow for security-critical race conditions and misalignments, such as privilege escalation, orphaned subtasks, or deadlocks due to cyclical delegation.

2. Temporal Logic Security Properties and Verification

The framework defines a comprehensive set of temporal logic properties for both the host agent and task lifecycle models. These invariants capture agentic security properties in the domains of safety, liveness, completeness, fairness, and reachability. Properties are formally specified in CTL/LTL and are amenable to model checking in tools such as NuSMV.

Host Agent Model Examples:

• Liveness: Every user request eventually yields a response:

$\mathtt{HP_1}:~ AG(Req_U \rightarrow AF\: Resp_H)$

• Safety (Access Control): All external entity invocations require validation:

$$\mathtt{HP_9}:~ AG(\mathsf{CL.invoke}(EE, protocol, payload) \rightarrow VM(EE))$$

• Data/Execution Mutual Exclusion: Subtasks with outstanding dependencies cannot be invoked:

$$\mathtt{HP_{10}}:~ \bigwedge_{i=1}^{n=|D|} AG(\mathsf{CL.invoke}(EE, protocol, sub\_task_i) \rightarrow dependencies[sub\_task_i]=\varnothing)$$

Task Lifecycle Model Example:

• Safety: A subtask may complete only from the in-progress state:

$$\mathtt{TL_6}:~ G((state = COMPLETED) \rightarrow (previous\_state = IN\ PROGRESS))$$

The semantics of these properties are domain-independent and protocol-agnostic, allowing them to serve as the foundation for both automated property checking and systematic code review across agentic system implementations.

3. Security Risk Mitigation via Formal Verification and Layered Controls

The unified semantic framework enables rigorous, automated evaluation of real-world agentic deployments. By mapping each architectural and execution element to a verifiable property or invariant, the framework:

• Detects privilege escalation or unauthorized access when property $\mathtt{HP_9}$ is violated.
• Identifies deadlocks and unproductive agent cycles via liveness/fairness checks ($$\mathtt{HP_{13/14}}, \mathtt{TL_1}, \mathtt{TL_{11}}$$).
• Pinpoints architectural misalignment, such as out-of-order protocol transitions or inconsistent state, when cross-protocol invariants are violated.
• Enables localization of security bugs: violations of host-agent properties indicate orchestration errors; task-lifecycle property violations signal faulty execution or error-handling logic.

Verification is made tractable by the explicit enumeration of relevant state and transition variables at both macro and micro levels. Model checking can exhaustively explore possible system traces, guaranteeing that no sequence of agent/communication actions can result in an unsafe or undesirable system state, assuming the properties are comprehensive and the models faithful.

4. Coordination Vulnerabilities and Architectural Misalignment

Architectural misalignment occurs when integration of agent-to-agent (A2A) and tool-invocation (MCP) protocols leads to inconsistent assumptions about state, trust, or sequence. The framework addresses:

• Task Handoff Failures: Requiring that all delegations are subject to validation and capability verification ($\mathtt{HP_9}$).
• Inconsistent State/Memory Views: Enforcing that no cross-layer transitions occur before DAG construction and only validated entities are invoked ($\mathtt{HP_{10}}$).
• Deadlocks / Circular Delegation: The DAG structure combined with liveness properties ($\mathtt{TL_1}$) preclude infinite wait cycles.
• Privilege Escalation: All indirect or delegated actions are conditional upon entity validation, blocking unauthorized propagation of privilege.

Security vulnerabilities thus manifest as formal property violations and can be systematically surfaced by trace analysis or bounded model checking on the joint state space.

5. Deployment Guidance, Tool Integration, and Performance Considerations

System developers can operationalize the framework as follows:

• Map protocol endpoints and code transitions to model constructs ($\mathcal{H}, \mathcal{L}$), instrumenting runtime checks and logs to align with temporal logic invariants.
• Integrate property-based tests during orchestration or task-planning module development, using model checkers to exhaustively verify possible workflow traces for compliance.
• Use failure traces from model checking to inform code refactoring, privilege separation, or protocol hardening interventions.
• Introduce layered validation modules at control points (Host Core, Registry, Orchestrator, Comm Layer) matching the layered property structure.
• Performance overhead is dominated by state-space model checking, which remains manageable due to the explicit decomposition and compositional modeling strategy.

The framework is robust for both high-assurance applications (finance, infrastructure, defense) and general-purpose agentic deployments, with scalability sustained by property modularity and clear locus-of-analysis assignment (global orchestration vs. local lifecycle).

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Agentic security, when grounded in jointly specified host agent and task lifecycle models with formal property assignments, yields a protocol-agnostic methodology for the detection, prevention, and mitigation of security-critical vulnerabilities in multi-agent AI systems. This systematic, verifiable approach addresses architectural misalignment, coordination flaws, and protocol-level threats that are otherwise opaque to traditional software security analyses, enabling practitioners to reliably deploy complex agentic workflows in adversarial or high-stakes contexts.