Agentic AI Security Review
Security Assessment for Autonomous AI Agents — Delegated Tool Use, Multi-Hop Instruction Injection, Trust Chain Integrity, and Memory Poisoning
Agentic AI systems plan, reason, take actions, use tools, invoke APIs, read and write files, execute code, browse the web — often without a human in the loop. This is a fundamentally different security problem than evaluating a chatbot or a classifier. The attack surface is not a single input-output interface: it is the entire chain of reasoning, delegation, and action that an agent can execute on behalf of a user or autonomously on a schedule. When an agent has authority to take actions in the world, the security of the agent is the security of everything it can touch.
The attack surface of an agentic system includes delegated tool use and the scope of what tools can do, multi-hop instruction flow where instructions passed between agents carry implicit trust, dynamic trust boundaries that shift based on what the agent retrieves from external sources, and non-deterministic execution paths that cannot be fully enumerated or tested statically. When an agent reads external content — a webpage, a document, an email — that content can contain adversarial instructions. When agents communicate, the trust chain is itself an attack surface. When an agent has persistent memory, that memory can be poisoned.
This review applies a purpose-built methodology for agentic systems, combining architecture analysis, trust boundary mapping, tool authorization review, and adversarial testing designed for non-deterministic behavior. Assessment is aligned to OWASP Top 10 for LLMs, MITRE ATLAS, and emerging agentic AI security standards. Every finding is demonstrated with a specific attack path and agent behavior — not theoretical risk. The output is a set of concrete architectural recommendations, guardrail specifications, and remediation guidance that engineering teams can implement.
Who This Is For
Ideal clients for this engagement.
The Problem
What this engagement addresses.
Delegated Tool Use
Agents are granted tool access — web browsing, code execution, database queries, API calls, file system access — to operate autonomously. The scope of what tools can do is often broader than what the agent is intended to do. Without precise authorization boundaries, agents can be manipulated into using their tools in ways that cause unintended harm.
Multi-Hop Instruction Injection
In multi-agent systems, a compromised or manipulated agent can pass adversarial instructions to downstream agents. The initial injection may occur in a benign-looking document, email, or webpage that one agent retrieves — and the adversarial instruction propagates through the system as each agent trusts the output of the previous one. Trust is transitive in ways that are not always intentional.
Memory Poisoning
Agents with persistent memory — vector stores, knowledge bases, conversation history, learned preferences — are vulnerable to memory poisoning attacks. An attacker who can influence what an agent writes to memory can persistently alter agent behavior, extract information over time, or establish hidden instructions that survive across sessions.
Non-Deterministic Behavior
Agents do not follow deterministic code paths. The same input may produce different tool invocations, reasoning chains, and actions across different runs. Traditional security testing methodologies that enumerate inputs and assert expected outputs are insufficient — agentic security testing requires adversarial exploration of the reasoning and planning space.
Inter-Agent Trust
In orchestrator-agent architectures, agents accept instructions from other agents. The trust model is often implicit: a sub-agent assumes that instructions from the orchestrator are legitimate. This creates an attack surface where compromising or impersonating one agent in the system provides access to the entire trust chain.
Deliverables
What you receive.
Agentic Architecture Security Review
Technical review of agent system architecture — orchestration patterns, agent roles and authority, inter-agent communication, external system integration, and trust boundary design. Identifies architectural patterns that introduce systemic risk regardless of individual component security.
Tool Use & Authorization Assessment
Evaluation of tool access granted to each agent — scope, authorization model, revocability, and least-privilege adherence. Identifies tools with excessive scope and authorization design patterns that allow privilege escalation or unintended action.
Adversarial Testing Results
Results of adversarial testing designed for agentic systems — multi-hop prompt injection attempts, memory poisoning probes, tool misuse sequences, and cross-agent trust exploitation. Each result includes the attack sequence, agent behavior observed, and impact assessment.
Trust Boundary Analysis
Mapping of all trust boundaries in the agentic system — where instructions originate, how they are validated, what authority they carry, and how that authority transfers through the agent chain. Identifies trust boundary violations and missing validation controls.
Remediation & Guardrail Recommendations
Specific remediation guidance for all findings, including architectural recommendations, guardrail specifications, tool authorization patterns, memory access controls, and human oversight mechanisms. Includes implementation guidance for common agentic frameworks.
Methodology
How the engagement works.
Architecture & Agent Discovery
Week 1
- Agent inventory — all agents, roles, capabilities, and authority in scope
- Architecture documentation review — orchestration design, inter-agent communication, and external integration
- Tool inventory — all tools accessible to agents with scope and permission documentation
- Memory and persistence mechanism review — vector stores, knowledge bases, and session state
Trust Boundary & Tool Use Analysis
Weeks 1 – 2
- Trust boundary mapping — instruction origin, validation logic, and authority propagation across the agent chain
- Tool authorization review — least-privilege analysis for all agent-accessible tools
- Inter-agent communication protocol and trust model evaluation
- Memory access control and poisoning risk assessment
- External content ingestion paths and indirect injection surface identification
Adversarial Testing
Weeks 2 – 4
- Direct prompt injection testing — override attempts against orchestrator and individual agents
- Indirect prompt injection — adversarial instructions embedded in documents, web content, emails, and API responses
- Multi-hop injection — propagating instructions through agent chains via trust relationships
- Memory poisoning probes — attempting to persistently alter agent behavior via memory writes
- Tool misuse and authorization boundary testing — manipulating agents to invoke tools outside intended scope
Reporting & Hardening
Week 4 – 5
- Agentic Architecture Security Review and Trust Boundary Analysis delivery
- Adversarial Testing Results and Tool Use Assessment delivery
- Remediation and Guardrail Recommendations delivery
- Live debrief with AI engineering and security teams — finding walkthrough and architectural recommendations
Engagement Tiers
Scoped to your architecture.
Focused
Single agent system — one agent or a tightly scoped orchestrator with one or two sub-agents. For organizations that need a targeted security review of a specific agentic deployment.
- Full assessment for in-scope agent system
- Agentic Architecture Security Review
- Tool Use & Authorization Assessment
- Adversarial testing — prompt injection, indirect injection, and tool misuse
- Remediation & Guardrail Recommendations
Comprehensive
Multi-agent system with complex orchestration, multiple tool integrations, and persistent memory. For teams deploying production agentic systems with real-world authority.
- Everything in Focused, applied to full multi-agent scope
- Trust Boundary Analysis across the full agent chain
- Multi-hop injection and inter-agent trust exploitation testing
- Memory poisoning assessment and persistent state security review
- Cross-agent authorization and privilege escalation analysis
Enterprise
Full agentic platform with multiple agent systems, shared infrastructure, governance requirements, and human oversight design. For enterprises deploying agentic AI at scale.
- Everything in Comprehensive, applied across the full agentic platform
- Cross-system trust boundary and shared infrastructure security review
- Human oversight mechanism design review and gap analysis
- Agentic AI incident response and monitoring guidance
- Governance recommendations for agentic system authorization policies
- Executive briefing on agentic AI risk posture and program recommendations
Prerequisites
- Agent system architecture documentation — orchestration design, agent roles, and tool inventory
- Access to agent system environments for adversarial testing — dedicated test environment strongly preferred over production
- Tool and API documentation for all agent-accessible integrations
- Memory and persistence mechanism documentation — vector store schemas, knowledge base structure, and session state design
Frequently Asked Questions
Common questions.
How is an Agentic AI Security Review different from an AI Security Assessment?
An AI Security Assessment covers the full AI security surface — model risk, data pipelines, application architecture, access controls, and governance. The Agentic AI Security Review is a specialized engagement for systems where AI agents plan, reason, and take actions with delegated authority. It applies purpose-built adversarial testing methodology for multi-hop injection, memory poisoning, and tool authorization failures that do not exist in non-agentic AI systems. We recommend completing an AI Security Assessment before or alongside the Agentic review for maximum coverage.
Can you test agents built on frameworks like LangChain, AutoGen, CrewAI, or custom orchestration?
Yes. The assessment methodology is framework-agnostic — it evaluates agent behavior, trust boundaries, and authorization design regardless of the underlying orchestration framework. We have experience with LangChain, LangGraph, AutoGen, CrewAI, OpenAI Assistants, Anthropic tool use, and custom orchestration systems. Framework-specific findings are noted where the implementation pattern introduces risk.
We want to test in our production environment because our test environment does not fully replicate agent behavior. Is that possible?
We can conduct the assessment in production, but we strongly recommend a dedicated test environment that replicates production tool integrations and agent configuration. Adversarial testing in production carries operational risk — agents have real tool access and real-world actions. Where production testing is necessary, we structure the engagement to minimize operational risk, test during low-traffic windows, and coordinate closely with engineering teams to monitor and halt agent actions if needed.
Our agents are still in development — is it too early for a security review?
No — earlier is better for agentic systems. The architectural recommendations from this review are most impactful before trust models, tool authorization designs, and memory architectures are finalized. Finding a systemic trust boundary design flaw in development costs hours to fix; finding it post-deployment may require rebuilding the orchestration architecture. We can structure the engagement as a security architecture review if the system is pre-deployment, with a follow-on adversarial testing phase when a stable test environment is available.
Related Offerings
Often paired with this engagement.
AI Security Assessment
Broader AI security assessment covering model risk, data pipeline security, architecture, access controls, and governance — the recommended precursor to this specialized agentic review.
Secure AI Architecture
Architecture design engagement for building AI and agentic systems with security controls, trust boundaries, and authorization models built in from the start.
AI Governance Program Build
Builds or matures the governance framework, policy, and oversight mechanisms needed to manage agentic AI deployments at organizational scale.
Ready to discuss this engagement?
30-minute discovery call. We will discuss your application architecture, your specific concerns, and whether this assessment is the right fit.