Classification registry
Threat taxonomy
Canonical categories used across AIVirusDB public records, grouped by the system and attack surface they describe.
- Version
- 1.0.0
- Released
- Aug 31, 2026
- Categories
- 14
- Public records
- 27
Candidate standards IDs are unverified risk alignments. They do not represent certification, compliance, or endorsement. See the standards crosswalk method.
Large language models
6 categoriesMalicious instructions embedded in untrusted content that attempt to influence model behavior.
Objective: Override intended instructions or redirect an agent's actions.
- Aliases
- INJECTION_PATTERN
- Candidate standards IDs
- owasp-llm-2025:LLM01
- Public records
- 1
- Last verified
- Sep 15, 2026
- Taxonomy version
- 1.0.0
Attempts to bypass model safety constraints or policy boundaries.
Objective: Cause the model to perform behavior that its safeguards prohibit.
- Aliases
- None
- Candidate standards IDs
- owasp-llm-2025:LLM01
- Public records
- 0
- Last verified
- No public records
- Taxonomy version
- 1.0.0
Unauthorized disclosure of training, retrieval, system, or user data.
Objective: Extract sensitive data, credentials, or protected context.
- Aliases
- DATA_EXFILTRATION
- Candidate standards IDs
- owasp-llm-2025:LLM02
- Public records
- 2
- Last verified
- Sep 15, 2026
- Taxonomy version
- 1.0.0
Role assignment or persona framing used to evade safety constraints.
Objective: Induce restricted behavior through a misleading role or scenario.
- Aliases
- None
- Candidate standards IDs
- owasp-llm-2025:LLM01
- Public records
- 0
- Last verified
- No public records
- Taxonomy version
- 1.0.0
Attempts to replace, suppress, or supersede system-level instructions and controls.
Objective: Gain higher-priority control over model or agent behavior.
- Aliases
- None
- Candidate standards IDs
- owasp-llm-2025:LLM01, owasp-llm-2025:LLM06
- Public records
- 0
- Last verified
- No public records
- Taxonomy version
- 1.0.0
Manipulation of retrieval data, embeddings, indexes, or ranking behavior.
Objective: Corrupt retrieved context or disclose vector-backed data.
- Aliases
- DATA_POISONING, RAG_POISONING
- Candidate standards IDs
- owasp-llm-2025:LLM04, owasp-llm-2025:LLM08
- Public records
- 0
- Last verified
- No public records
- Taxonomy version
- 1.0.0
Model Context Protocol
3 categoriesManipulation of MCP tool metadata, descriptions, parameters, or responses.
Objective: Steer an agent into unsafe or unauthorized tool use.
- Aliases
- MCP_TOOL_ABUSE
- Candidate standards IDs
- owasp-llm-2025:LLM03, owasp-llm-2025:LLM06
- Public records
- 0
- Last verified
- No public records
- Taxonomy version
- 1.0.0
MCP_RUG_PULL
MCP rug pull
activeA trusted MCP tool changes behavior after installation or approval.
Objective: Exploit a previously trusted integration through an unreviewed change.
- Aliases
- None
- Candidate standards IDs
- owasp-llm-2025:LLM03
- Public records
- 0
- Last verified
- No public records
- Taxonomy version
- 1.0.0
An MCP integration is induced to use authority beyond the caller's intended permissions.
Objective: Escalate privileges through delegated tool authority.
- Aliases
- None
- Candidate standards IDs
- owasp-llm-2025:LLM06
- Public records
- 2
- Last verified
- Sep 16, 2026
- Taxonomy version
- 1.0.0
Agents and supply chain
2 categoriesPersistent malicious prompts or instructions that propagate through agent memory or workflows.
Objective: Maintain control or spread unsafe behavior across agent executions.
- Aliases
- None
- Candidate standards IDs
- owasp-llm-2025:LLM01, owasp-llm-2025:LLM06
- Public records
- 0
- Last verified
- No public records
- Taxonomy version
- 1.0.0
Compromise of dependencies, packages, models, or distribution channels.
Objective: Introduce malicious behavior through a trusted delivery path.
- Aliases
- BACKDOOR_ATTACK
- Candidate standards IDs
- owasp-llm-2025:LLM03
- Public records
- 2
- Last verified
- Sep 16, 2026
- Taxonomy version
- 1.0.0
Framework vulnerabilities
2 categoriesA disclosed vulnerability affecting the Hermes agent framework.
Objective: Exploit a framework defect to compromise agent execution or data.
- Aliases
- None
- Candidate standards IDs
- None registered
- Public records
- 0
- Last verified
- No public records
- Taxonomy version
- 1.0.0
An exploit affecting the OpenClaw agent framework or its supported integrations.
Objective: Compromise agent execution, data, or tool authority through a known exploit.
- Aliases
- None
- Candidate standards IDs
- None registered
- Public records
- 24
- Last verified
- Oct 16, 2026
- Taxonomy version
- 1.0.0
Adversarial machine learning
1 categoryAdversarial inputs or perturbations designed to alter machine-learning model behavior.
Objective: Cause model evasion, misclassification, or unsafe downstream decisions.
- Aliases
- ADVERSARIAL_INPUT, ADVERSARIAL_ML
- Candidate standards IDs
- None registered
- Public records
- 0
- Last verified
- No public records
- Taxonomy version
- 1.0.0