Agent Graph Model
Written model of your agent topology, trust boundaries, and tool access map. Useful as ongoing reference for your engineering team beyond the engagement.
15–25 pages · Markdown + diagramSERVICES · OFFENSIVE← BACK TO SERVICES
Multi-Agent Attack Simulation
Adversarial campaigns against your agent graph — testing chained injection, delegation abuse, and cross-agent privilege escalation.
Multi-Agent Attack Simulation is purpose-built for systems where multiple AI agents communicate, delegate, and act through tool calls. We treat the agent graph as a single attack surface — finding the trust assumptions between agents that an attacker can exploit. This is the 2026-specific engagement: most AI security work focuses on single models, but the real losses are happening between agents.
// THE PROBLEM
What we're solving when you hire us for thisWhen you connect multiple AI agents — orchestrator agents, specialist agents, tool-calling agents — you create something that no single-model security audit can evaluate. Agent A might be hardened against injection. Agent B might be hardened too. But the path from A to B — the messages they exchange, the trust they inherit, the tool calls they delegate — is where 2026's exploits actually live.
Multi-Agent Attack Simulation tests this surface specifically. We model your agent graph, identify trust boundaries, design adversarial scenarios that escalate across agents, and execute them against your deployment. The findings are about your graph topology, not your individual model choice.
// HOW WE RUN IT
The five phases of a Multi-Agent Attack Simulation engagement01
Graph Reconstruction
We map your agent topology: which agents exist, what tools each has access to, how they delegate, what trust assumptions are baked in, and where untrusted data enters the graph. Output is a written graph model approved by you before testing.
Duration 4–6 days · Output: graph model + approval gate02
Attack Scenario Design
Based on the graph, we design scenarios where an attacker compromises an entry-point agent and attempts to escalate through delegation, tool inheritance, or cross-agent context bleed. Scenarios are categorized and disclosed at category level before testing.
Duration 3–4 days · Output: scenario catalog03
Scenario Execution
We execute scenarios against your live deployment in agreed test windows. Each scenario is logged with full agent traces — inputs, outputs, tool calls, state changes — so findings are reproducible.
Duration 7–10 days · Output: traced findings04
Escalation Path Analysis
For each successful scenario, we document the full escalation path: which agent was compromised first, how trust was inherited, what an attacker ultimately could accomplish. Severity reflects realistic impact across the graph, not isolated impact per agent.
Duration 3–5 days · Output: per-path analysis05
Reporting & Architecture Guidance
Final report includes findings, escalation paths, and architectural recommendations specific to your graph topology — not generic 'use less trust' advice.
Duration 4–5 days · Output: report + architecture session// WHAT YOU RECEIVE
Deliverables, named and specificFindings Document
Each successful attack scenario documented: entry point, escalation path, agents traversed, ultimate impact, and reproduction steps.
40–60 pages · Markdown + PDFExecutive Summary
Sanitized summary of escalation paths and risk categories suitable for board reporting or compliance documentation.
2–3 pages · Markdown + PDFArchitectural Recommendations
Graph-topology-specific recommendations for trust isolation, tool scoping, and delegation hardening — specific to your system, not generic advice.
Recommendation document + roadmapScenario Reproduction Bundle
Each scenario is reproducible against your deployment so your team can verify fixes and confirm that architectural changes prevent regression.
Per-scenario documentationArchitecture Working Session
Working session with your engineering team to walk through findings and discuss architectural changes needed to close the identified escalation paths.
90-minute session + recording// ENGAGEMENT SHAPE
Specific numbers, not approximations4–6 weeks
Graph complexity drives timeline2 practitioners
Minimum, both seniorDaily async updates
By 18:00 client timezone< 4 hours
Notification, not remediationGraph + tool surface
Defined in SOW$32,500
Reflects engagement complexity< 5 business days
After engagement close30 days default
Agent traces deleted at close// WHEN THIS IS RIGHT
Honest fit criteria// THE RIGHT FIT
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Your production AI system uses 3+ agents that communicate or delegate (CrewAI, LangGraph, AutoGen, or custom orchestration).
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Single-model security testing has cleared your individual agents but you suspect the inter-agent surface is unmapped.
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You're shipping a multi-agent system to a regulated environment and need defensible testing of the agent graph specifically.
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You've had a security incident where the attack traversed multiple agents and need to understand how it happened.
// THE WRONG FIT
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Your AI deployment uses a single model with no agent-to-agent communication — Adversarial Probing covers that surface better.
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Your agent system is still in development without a stable topology — the graph needs to be deployed before it can be tested.
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You need fast turnaround — graph engagements are 4–6 weeks minimum due to topology complexity. We won't compress this.
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Your agents don't share trust or context — if every agent is fully isolated, there's no inter-agent surface to attack.
// RELATED ENGAGEMENTS
Where this connects to the rest of our workAgentic Guardrails
The defensive counterpart — once you understand the attack paths, this engagement designs runtime constraints to prevent them.
DETAILS →
Adversarial Probing
If you need both per-agent testing and graph testing, the two engagements pair well as a combined campaign.
DETAILS →
Incident Response
If a multi-agent system is actively under attack, this is the engagement for triaging the live incident.
DETAILS →
Multi-Agent Attack Simulation engagements start from $32,500. Reply within 24h. NDA before scope.
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