MAESTRO

02 - Threat Taxonomy

Version: 1.0.0

This document consolidates the OWASP Agentic Security Initiative (ASI) core threat taxonomy (T1-T15), the extended threat catalog per MAESTRO layer (T16-T47), and blindspot threat vectors identified through supplemental analysis. Together they form the comprehensive threat reference for MAESTRO-based threat modeling.


Table of Contents

  1. ASI Core Threat Taxonomy (T1-T15)
  2. Extended Threat Catalog Per Layer (T16-T47)
  3. Blindspot Vectors
  4. Versioning Note

ASI Core Threat Taxonomy (T1-T15)

The OWASP Agentic Security Initiative (ASI) defines 15 core agentic threats:

ID Threat Name Description Key Indicators
T1 Memory Poisoning Malicious modification of agent memory/training data corrupts decision-making Shared memory, fine-tuning, RAG knowledge bases
T2 Tool Misuse Agent’s authorized tools exploited for unintended/malicious purposes MCP tools, function calling, API access
T3 Privilege Compromise Exploitation of agent’s elevated permissions for unauthorized actions Service accounts, IAM roles, chained authorization
T4 Resource Overload Overwhelming agent operations through coordinated resource exhaustion API quotas, compute limits, concurrent executions
T5 Cascading Hallucinations Agent generates false outputs that propagate through the system Non-deterministic LLM, no output validation
T6 Intent Breaking & Goal Manipulation Manipulation of agent’s goals/objectives through adversarial inputs Prompt injection, workflow bypass, planning exploitation
T7 Misaligned & Deceptive Behaviour Agent deviates from intended behavior/policies Compliance drift, non-determinism, adversarial context
T8 Repudiation & Untraceability Agent actions cannot be traced or attributed Missing audit logs, no non-repudiation controls
T9 Identity Spoofing Impersonation of agents or users in the system Weak authentication, credential theft, token replay
T10 Overwhelming HITL Human reviewers overwhelmed by volume, reducing oversight quality High-volume agent output, rubber-stamping risk
T11 Unexpected RCE / Code Attacks Agent generates/executes malicious code Code generation tools, eval(), dynamic execution
T12 Agent Communication Poisoning Corruption of inter-agent or agent-external communications Shared data stores, email injection, API manipulation
T13 Rogue Agents Malicious agent introduced or existing agent compromised Plugin compromise, supply chain, agent registry
T14 Human Attacks on MAS Humans exploit agent systems through crafted requests Social engineering via agent, confused deputy
T15 Human Trust Manipulation Exploitation of human over-reliance on AI outputs Automation bias, authority deference, trust erosion

Extended Threat Catalog Per Layer (T16-T47)

Beyond the ASI T1-T15, MAESTRO discovers extended threat scenarios. These are threats not fully covered by the base taxonomy. Use this as a checklist during each layer analysis.

Note: Extended threats with T-IDs (T16-T47) are formally cataloged entries. Unlabeled bullet items are additional threat considerations from the source material that have not been formally numbered.

Layer 1: Foundation Model - Extended Threats

Layer 2: Data Operations - Extended Threats

Layer 3: Agent Frameworks - Extended Threats

Layer 4: Deployment Infrastructure - Extended Threats

Layer 5: Evaluation & Observability - Extended Threats

Layer 6: Security & Compliance - Extended Threats

Layer 7: Agent Ecosystem - Extended Threats

Note: Threat IDs T26-T27 are reserved for future assignment to emerging threat categories. These IDs were held back during the initial taxonomy development to allow for threats that span multiple layers or represent novel attack patterns not yet observed in production agentic systems. Organizations discovering threats that do not map to T1-T25 or T28-T47 should document them using project-local identifiers (e.g., “ORG-T1”) and propose formal T-ID assignment through the OWASP ASI project.


Blindspot Vectors

The following threat vectors represent gaps not adequately covered by the ASI T1-T15 taxonomy or the extended catalog above. They were identified through analysis of emerging attack patterns against agentic AI systems, MCP protocol deployments, and multi-tenant agent architectures.

# Vector Name Description Affected Layers
BV-1 Context Window Poisoning Attacks that fill the LLM context window with adversarial content to push legitimate instructions out of the effective attention window. As context windows grow, this becomes a scalable way to override system prompts and safety instructions without classic prompt injection. L1, L3
BV-2 Tool Description Poisoning (Rug Pull) An MCP server changes its tool descriptions after trust has been established to alter LLM invocation semantics. The agent has already been approved to use the tool, but the tool’s behavior silently changes. This exploits the gap between trust-time and use-time verification. L3, L7
BV-3 Agentic Supply Chain (Dependency Confusion) npm/pip supply chain attacks targeting agent framework or MCP server dependencies. An attacker publishes a malicious package with a name similar to or identical to a private dependency, causing the agent’s build pipeline to install the attacker’s code. L3, L4, L7
BV-4 Prompt Leakage via Tool Outputs Agent system prompt extracted through crafted tool response payloads. A malicious or compromised tool returns output specifically designed to cause the LLM to echo its system prompt in subsequent responses or tool calls. L1, L3
BV-5 Multi-Tenant Agent Isolation Failure Conversation context bleed, session hijacking, or cross-tenant data leakage in shared agent instances. When multiple users or tenants share an agent deployment, insufficient isolation allows one tenant to observe or influence another’s interactions. L3, L4, L6
BV-6 Cost/Budget Exhaustion Attacks Targeting billing by triggering expensive API calls, large model inference, or token-intensive prompts. An attacker crafts inputs that maximize token consumption or trigger repeated expensive tool calls, running up costs without necessarily compromising data or functionality. L1, L4
BV-7 Agent Memory Injection via A2A Google A2A protocol and similar agent-to-agent standards introduce new memory injection vectors not covered by T1 or T12. An agent receiving A2A messages may incorporate the content into its working memory or context, allowing a malicious peer to inject persistent false beliefs. L2, L7
BV-8 Steganographic Data Exfiltration Agent encodes sensitive data in seemingly benign outputs using invisible unicode characters, zero-width joiners, or formatting patterns that are imperceptible to human reviewers but machine-readable by an external receiver. L1, L5, L6
BV-9 Time-of-Check-to-Time-of-Use (TOCTOU) Permissions change between when the agent checks authorization and when it executes the authorized action. In agentic systems with asynchronous workflows, the window between check and use can be substantial, creating exploitable race conditions. L3, L6
BV-10 LLM Reasoning Manipulation Attacks on chain-of-thought, planning steps, or scratchpad content that manipulate the model’s reasoning process without altering the user-visible prompt directly. By injecting content into intermediate reasoning artifacts, an attacker can steer decisions while leaving the prompt and output superficially normal. L1, L3
BV-11 OAuth/OIDC Token Relay Attacks Delegation attacks where OAuth tokens are relayed through agent chains beyond the confused deputy pattern. In multi-hop agent delegation, tokens may be forwarded through intermediary agents that gain unintended access, or tokens scoped for one service are presented to another. L6, L7
BV-12 Observability Overload Generating excessive legitimate-looking log entries to hide malicious activity. This is the inverse of log deletion (T23): rather than removing evidence, the attacker floods observability systems with noise so that genuine indicators of compromise are buried and never investigated. L5

Versioning Note

Note: Extended threat IDs (T16-T47) and blindspot vectors are project-local identifiers from this playbook’s analysis. They are not part of the official OWASP ASI taxonomy and may diverge from future official releases. When referencing these identifiers externally, always clarify that they originate from this playbook rather than from the OWASP ASI specification. The core taxonomy (T1-T15) is authoritative as of OWASP ASI v1.0 (April 2025).


Attribution: OWASP GenAI Security Project - Multi-Agentic System Threat Modelling Guide. Licensed under CC BY-SA 4.0.


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