Agent SkillsAgent Skills
anshumanbh

agentic-security-threat-modeling

@anshumanbh/agentic-security-threat-modeling
anshumanbh
263
61 forks
Updated 3/31/2026
View on GitHub

Identify agentic AI security threats based on OWASP Top 10 for Agentic Applications 2026. Use when analyzing AI agents, LLM-powered applications, chatbots, auto-reply systems, tool-using AI, browser automation, sandbox execution, or any application that uses AI/LLM APIs (Anthropic, OpenAI, Claude, GPT) to process user input and take actions.

Installation

$npx agent-skills-cli install @anshumanbh/agentic-security-threat-modeling
Claude Code
Cursor
Copilot
Codex
Antigravity

Details

Repositoryanshumanbh/securevibes
Pathpackages/core/securevibes/skills/threat-modeling/agentic-security/SKILL.md
Branchmain
Scoped Name@anshumanbh/agentic-security-threat-modeling

Usage

After installing, this skill will be available to your AI coding assistant.

Verify installation:

npx agent-skills-cli list

Skill Instructions

name: agentic-security-threat-modeling description: Identify agentic AI security threats based on OWASP Top 10 for Agentic Applications 2026. Use when analyzing AI agents, LLM-powered applications, chatbots, auto-reply systems, tool-using AI, browser automation, sandbox execution, or any application that uses AI/LLM APIs (Anthropic, OpenAI, Claude, GPT) to process user input and take actions. allowed-tools: Read, Grep, Glob, Write

Agentic Security Threat Modeling Skill

Purpose

Augment generic STRIDE threat modeling with agentic AI-specific threats from OWASP Top 10 for Agentic Applications 2026. This skill identifies security risks unique to autonomous AI agents, multi-agent systems, and LLM-powered applications.

When to Use This Skill

IMPORTANT: Activate this skill when the codebase contains ANY of these patterns. This includes both standard frameworks AND custom agent implementations.

Agent Framework Indicators

  • langchain imports or dependencies
  • autogen imports or dependencies
  • crewai imports or dependencies
  • claude_agent_sdk or claude-agent-sdk usage
  • openai.beta.assistants or OpenAI Assistants API
  • semantic_kernel imports
  • llama_index with agent patterns

LLM/AI API Usage (Custom Implementations)

  • anthropic or @anthropic-ai/sdk imports (Claude API)
  • openai imports (OpenAI/GPT API)
  • Anthropic( or OpenAI( client instantiation
  • messages.create, chat.completions.create API calls
  • Any code calling Claude, GPT, or other LLM APIs

Custom Agent Patterns (Non-Framework)

  • Files or directories named agent, agents, runner, executor
  • Classes with Agent, Runner, Executor, Bot, Assistant in name
  • Tool execution code (bash-tools, browser-tools, tool-use)
  • Sandbox/container execution for AI (sandbox, isolated, container)
  • Auto-reply or chatbot systems (auto-reply, reply, bot, chatbot)

Agentic Code Patterns

  • Tool/function definitions for LLMs (@tool, tools=[], tools:, function calling)
  • Agent class definitions (class.*Agent, BaseAgent, .*Runner, .*Executor)
  • Multi-agent communication (agent.send, agent.receive, inter-agent messaging)
  • Memory/context management (memory, context, ConversationBuffer, session, transcript)
  • RAG implementations (retriever, VectorStore, embedding)
  • Prompt templates with tool instructions (system_prompt, systemPrompt)
  • Agent orchestration (AgentExecutor, run_agent, runAgent, workflow definitions)
  • Browser automation for AI (playwright, puppeteer, browser-tool)

MCP (Model Context Protocol) Patterns

  • MCP server implementations
  • Tool registration for MCP
  • MCP client connections

Detection Phase

Before generating threats, scan the codebase for agentic patterns using these searches:

1. Search for LLM API usage (CRITICAL - catches custom implementations):
   - Grep for: anthropic|openai|claude|gpt-|llm|completion|chat\.create|messages\.create
   
2. Search for framework imports:
   - Grep for: langchain|autogen|crewai|claude_agent_sdk|openai.*assistant|semantic_kernel
   
3. Search for agent/runner/executor patterns:
   - Grep for: agent|runner|executor|bot|assistant (in file/directory names)
   - Grep for: class.*Agent|class.*Runner|class.*Executor|class.*Bot
   
4. Search for tool execution patterns:
   - Grep for: bash.tool|browser.tool|tool.use|tools\s*[:=]|@tool|function.call
   
5. Search for sandbox/isolation patterns:
   - Grep for: sandbox|container|isolated|docker.*run
   
6. Search for auto-reply/chatbot patterns:
   - Grep for: auto.reply|chatbot|bot.reply|message.handler
   
7. Search for memory/session patterns:
   - Grep for: memory|context|session|transcript|conversation
   
8. Search for MCP patterns:
   - Grep for: mcp|MCPServer|MCPClient|tool_registration

If ANY of these patterns are found, proceed with agentic threat modeling using ASI01-ASI10 categories.

OWASP Agentic Security Initiative (ASI) Threat Categories

ASI01: Agent Goal Hijack

Description: Attacks that exploit prompt injection to override, modify, or manipulate the intended goal of an AI Agent, diverting activities toward attacker-specified objectives.

Code Patterns Indicating Risk:

  • User input directly concatenated into prompts
  • No input sanitization before LLM processing
  • Missing guardrails against instruction override
  • Prompts without clear system/user separation

Threat Template:

{
  "id": "THREAT-ASI01-XXX",
  "category": "Tampering",
  "title": "Agent Goal Hijack via [injection point]",
  "description": "Attacker can inject malicious instructions through [input source] to override agent's intended goal and execute unauthorized actions",
  "severity": "critical",
  "affected_components": ["[agent component]", "[input handler]"],
  "attack_scenario": "1. Attacker crafts input containing hidden instructions\n2. Input reaches agent without sanitization\n3. Agent interprets injected content as instructions\n4. Agent executes attacker's goal instead of intended task",
  "vulnerability_types": ["CWE-74", "CWE-77"],
  "mitigation": "Implement input sanitization, use structured prompts with clear boundaries, add output validation, implement goal state monitoring",
  "existing_controls": [],
  "control_effectiveness": "none",
  "attack_complexity": "low",
  "likelihood": "high",
  "impact": "critical",
  "risk_score": "critical",
  "residual_risk": "Without input sanitization or prompt boundaries, full goal hijack is possible"
}

ASI02: Tool Misuse and Exploitation

Description: Agents misuse legitimate tools due to prompt injection, misalignment, or unsafe delegation, leading to data exfiltration, tool output manipulation, or workflow hijacking.

Code Patterns Indicating Risk:

  • Tools with broad permissions (file system, network, database)
  • No input validation on tool parameters
  • Missing rate limits on tool invocations
  • Tools that accept URLs or file paths from user input

Threat Template:

{
  "id": "THREAT-ASI02-XXX",
  "category": "Tampering",
  "title": "Tool Misuse via [tool name]",
  "description": "Agent can be manipulated to misuse [tool name] for unauthorized [action type]",
  "severity": "high",
  "affected_components": ["[tool]", "[agent executor]"],
  "attack_scenario": "1. Attacker provides input referencing tool capabilities\n2. Agent invokes tool with attacker-controlled parameters\n3. Tool performs unintended action (data exfil, SSRF, etc.)",
  "vulnerability_types": ["CWE-918", "CWE-78", "CWE-22"],
  "mitigation": "Implement least-privilege tool profiles, add input filtering, require human approval for sensitive actions, sandbox tool execution",
  "existing_controls": [],
  "control_effectiveness": "none",
  "attack_complexity": "medium",
  "likelihood": "high",
  "impact": "high",
  "risk_score": "critical",
  "residual_risk": "Tools with broad permissions can be exploited for data exfiltration or lateral movement"
}

ASI03: Identity and Privilege Abuse

Description: Exploits dynamic trust and delegation in agents to escalate access by manipulating delegation chains, role inheritance, or cached credentials.

Code Patterns Indicating Risk:

  • Agents inheriting user credentials or tokens
  • Long-lived tokens passed to agents
  • No credential scoping per task
  • Shared identity across multiple agents
  • Missing delegation chain validation

Threat Template:

{
  "id": "THREAT-ASI03-XXX",
  "category": "Elevation of Privilege",
  "title": "Privilege Escalation via [delegation mechanism]",
  "description": "Agent inherits excessive privileges through [mechanism], enabling unauthorized access to [resource]",
  "severity": "high",
  "affected_components": ["[agent]", "[auth component]"],
  "attack_scenario": "1. Agent receives broad credentials for convenience\n2. Attacker exploits agent vulnerability\n3. Attacker gains access to all resources agent can access",
  "vulnerability_types": ["CWE-269", "CWE-250", "CWE-266"],
  "mitigation": "Use per-task identities, implement JIT privileges, segment memory, validate delegation chains",
  "existing_controls": [],
  "control_effectiveness": "none",
  "attack_complexity": "medium",
  "likelihood": "medium",
  "impact": "high",
  "risk_score": "high",
  "residual_risk": "Inherited credentials provide lateral movement opportunities if agent is compromised"
}

ASI04: Agentic Supply Chain Vulnerabilities

Description: Vulnerabilities from external components—models, plugins, tools, data sources—that are not adequately vetted or monitored.

Code Patterns Indicating Risk:

  • Third-party MCP servers or plugins
  • Unpinned model versions
  • External tool registries
  • RAG sources from untrusted origins
  • Dynamic plugin loading

Threat Template:

{
  "id": "THREAT-ASI04-XXX",
  "category": "Tampering",
  "title": "Supply Chain Compromise via [component]",
  "description": "Malicious or compromised [component type] could inject backdoors or exfiltrate data",
  "severity": "high",
  "affected_components": ["[external component]", "[integration point]"],
  "attack_scenario": "1. Attacker compromises third-party [component]\n2. Agent loads/uses compromised component\n3. Malicious code executes in agent context",
  "vulnerability_types": ["CWE-829", "CWE-494", "CWE-1104"],
  "mitigation": "Vet dependencies, pin versions, verify model provenance, use SBOM/AIBOM, scan for vulnerabilities",
  "existing_controls": [],
  "control_effectiveness": "none",
  "attack_complexity": "high",
  "likelihood": "medium",
  "impact": "high",
  "risk_score": "high",
  "residual_risk": "Third-party components may contain backdoors or vulnerabilities not detected by standard scanning"
}

ASI05: Unexpected Code Execution (RCE)

Description: Agent executes code not intended by designers, through prompt injection, tool misuse, or insecure code execution features.

Code Patterns Indicating Risk:

  • eval() or exec() on LLM output
  • Code interpreter tools
  • Shell command execution from agent
  • Dynamic code generation without sandboxing

Threat Template:

{
  "id": "THREAT-ASI05-XXX",
  "category": "Tampering",
  "title": "Remote Code Execution via [execution path]",
  "description": "Agent can be tricked into executing arbitrary code through [mechanism]",
  "severity": "critical",
  "affected_components": ["[code execution component]"],
  "attack_scenario": "1. Attacker crafts input that generates malicious code\n2. Agent passes generated code to execution engine\n3. Malicious code runs with agent's privileges",
  "vulnerability_types": ["CWE-94", "CWE-95", "CWE-78"],
  "mitigation": "Disable unnecessary code execution, sandbox all generated code, validate code before execution, use allowlists",
  "existing_controls": [],
  "control_effectiveness": "none",
  "attack_complexity": "low",
  "likelihood": "high",
  "impact": "critical",
  "risk_score": "critical",
  "residual_risk": "Code execution without sandboxing allows full system compromise"
}

ASI06: Memory & Context Poisoning

Description: Attackers corrupt persistent memory, session context, or RAG knowledge base to influence future agent behavior across sessions.

Code Patterns Indicating Risk:

  • Persistent conversation memory
  • RAG with user-contributed content
  • Shared context across users/sessions
  • No memory validation or expiration

Threat Template:

{
  "id": "THREAT-ASI06-XXX",
  "category": "Tampering",
  "title": "Context Poisoning via [memory component]",
  "description": "Attacker can inject malicious content into [memory type] that persists and influences future agent behavior",
  "severity": "high",
  "affected_components": ["[memory/RAG component]"],
  "attack_scenario": "1. Attacker injects crafted content into agent memory\n2. Malicious content persists across sessions\n3. Future interactions influenced by poisoned context",
  "vulnerability_types": ["CWE-472", "CWE-915"],
  "mitigation": "Validate memory inputs, segment by user/tenant, apply TTLs, monitor for anomalies",
  "existing_controls": [],
  "control_effectiveness": "none",
  "attack_complexity": "medium",
  "likelihood": "medium",
  "impact": "high",
  "risk_score": "high",
  "residual_risk": "Poisoned context can influence agent behavior across multiple sessions and users"
}

ASI07: Insecure Inter-Agent Communication

Description: Vulnerabilities when agents exchange messages without proper authentication, encryption, or validation.

Code Patterns Indicating Risk:

  • Agent-to-agent messaging without auth
  • Plaintext inter-agent communication
  • No message signing or verification
  • Open agent discovery/registration

Threat Template:

{
  "id": "THREAT-ASI07-XXX",
  "category": "Spoofing",
  "title": "Inter-Agent Message Injection via [communication channel]",
  "description": "Attacker can inject or spoof messages between agents through [channel]",
  "severity": "high",
  "affected_components": ["[agent A]", "[agent B]", "[communication layer]"],
  "attack_scenario": "1. Attacker intercepts/injects inter-agent messages\n2. Receiving agent trusts spoofed message\n3. Agent performs unauthorized action based on fake request",
  "vulnerability_types": ["CWE-290", "CWE-319", "CWE-345"],
  "mitigation": "Use mTLS for agent communication, sign all messages, validate peer identities, encrypt channels",
  "existing_controls": [],
  "control_effectiveness": "none",
  "attack_complexity": "medium",
  "likelihood": "medium",
  "impact": "high",
  "risk_score": "high",
  "residual_risk": "Spoofed inter-agent messages can trigger unauthorized actions across the agent network"
}

ASI08: Cascading Failures

Description: Faults in one agent propagate through interconnected systems, amplifying impact and causing widespread disruption.

Code Patterns Indicating Risk:

  • Tightly coupled agent dependencies
  • No circuit breakers or bulkheads
  • Shared resources without isolation
  • No retry limits or timeouts

Threat Template:

{
  "id": "THREAT-ASI08-XXX",
  "category": "Denial of Service",
  "title": "Cascading Failure via [failure point]",
  "description": "Failure in [component] can cascade to [dependent components], causing system-wide disruption",
  "severity": "medium",
  "affected_components": ["[origin component]", "[dependent components]"],
  "attack_scenario": "1. Attacker triggers failure in [component]\n2. Failure propagates to dependent agents\n3. System-wide outage or degradation occurs",
  "vulnerability_types": ["CWE-754", "CWE-400"],
  "mitigation": "Implement circuit breakers, add bulkheads, validate outputs, apply timeouts and rate limits",
  "existing_controls": [],
  "control_effectiveness": "none",
  "attack_complexity": "medium",
  "likelihood": "medium",
  "impact": "medium",
  "risk_score": "medium",
  "residual_risk": "Tightly coupled agents without circuit breakers can cause system-wide outages"
}

ASI09: Human-Agent Trust Exploitation

Description: Attackers leverage trust humans place in AI agents to deceive, manipulate, or social-engineer users.

Code Patterns Indicating Risk:

  • Agent outputs presented as authoritative
  • No source attribution on agent responses
  • Agent can impersonate users or systems
  • Missing confidence indicators

Threat Template:

{
  "id": "THREAT-ASI09-XXX",
  "category": "Spoofing",
  "title": "Trust Exploitation via [agent capability]",
  "description": "Agent can be manipulated to present false information authoritatively, misleading users into [harmful action]",
  "severity": "medium",
  "affected_components": ["[agent]", "[user interface]"],
  "attack_scenario": "1. Attacker manipulates agent output\n2. User trusts agent-presented information\n3. User takes harmful action based on false info",
  "vulnerability_types": ["CWE-451"],
  "mitigation": "Add output attribution, show confidence levels, require human review for high-stakes decisions",
  "existing_controls": [],
  "control_effectiveness": "none",
  "attack_complexity": "low",
  "likelihood": "medium",
  "impact": "medium",
  "risk_score": "medium",
  "residual_risk": "Users may act on false information presented authoritatively by the agent"
}

ASI10: Rogue Agents

Description: Agents acting outside intended scope due to compromise, misconfiguration, emergent behavior, or malicious design.

Code Patterns Indicating Risk:

  • No agent behavior monitoring
  • Missing kill switches
  • No agent registration/inventory
  • Agents can spawn sub-agents without limits

Threat Template:

{
  "id": "THREAT-ASI10-XXX",
  "category": "Repudiation",
  "title": "Rogue Agent Behavior in [agent]",
  "description": "[Agent] could act outside intended scope due to [cause], performing unauthorized actions without detection",
  "severity": "high",
  "affected_components": ["[agent]"],
  "attack_scenario": "1. Agent behavior drifts from intended scope\n2. Rogue actions go undetected due to missing monitoring\n3. Agent causes damage before intervention",
  "vulnerability_types": ["CWE-778"],
  "mitigation": "Maintain agent inventory, monitor behavior baselines, implement kill switches, use signed artifacts",
  "existing_controls": [],
  "control_effectiveness": "none",
  "attack_complexity": "medium",
  "likelihood": "medium",
  "impact": "high",
  "risk_score": "high",
  "residual_risk": "Without monitoring and kill switches, rogue agent behavior may go undetected"
}

Output Format

Generate additional threats in the same JSON format as the base threat model:

{
  "id": "THREAT-ASI0X-001",
  "category": "[STRIDE category]",
  "title": "[Specific threat title]",
  "description": "[Detailed description]",
  "severity": "critical|high|medium|low",
  "affected_components": ["component1", "component2"],
  "attack_scenario": "[Step-by-step attack]",
  "vulnerability_types": ["CWE-XXX"],
  "mitigation": "[Specific recommendations]",
  "existing_controls": ["[Controls found in codebase]"],
  "control_effectiveness": "none|partial|substantial",
  "attack_complexity": "low|medium|high",
  "likelihood": "low|medium|high",
  "impact": "low|medium|high|critical",
  "risk_score": "low|medium|high|critical",
  "residual_risk": "[Description of remaining risk after existing controls]"
}

ID Convention: Use THREAT-ASI0X-NNN where X is the ASI category number (1-10).

Risk Assessment Fields:

  • existing_controls: List controls found in the codebase (e.g., sandboxing, rate limiting, input validation)
  • control_effectiveness: How well existing controls mitigate the threat
  • attack_complexity: Skill level required to exploit (low = script kiddie, high = expert)
  • likelihood: Probability of exploitation given controls and complexity
  • impact: Damage if successfully exploited
  • risk_score: Calculated from likelihood × impact matrix
  • residual_risk: What risk remains even with existing controls

Workflow

  1. Detect Agentic Patterns: Search codebase for framework imports and agent patterns
  2. Identify Components: Map detected patterns to specific files and components
  3. Apply ASI Categories: For each component, evaluate all 10 ASI categories
  4. Generate Threats: Create threats using templates, customized to specific codebase
  5. Prioritize: Assign severity based on exploitability and impact
  6. Output: Add threats to THREAT_MODEL.json alongside STRIDE threats

Examples

See examples.md for comprehensive attack scenarios for each ASI category with real-world code patterns.

References

More by anshumanbh

View all
sql-injection-testing
263

Validate SQL injection vulnerabilities (including blind SQLi) across time-based, error-based, boolean-based, UNION-based, stacked-query, and out-of-band patterns. Use when testing CWE-89 (SQL Injection), CWE-564 (Hibernate SQL Injection), and related SQL injection classes across MySQL, PostgreSQL, MSSQL, Oracle, and SQLite targets.

xss-testing
263

Validate Cross-Site Scripting (XSS) vulnerabilities including Reflected, Stored, and DOM-based XSS. Test by injecting script payloads into user-controlled inputs and observing if they execute in browser context. Use when testing CWE-79 (XSS), CWE-80 (Basic XSS), CWE-81 (Error Message XSS), CWE-83 (Attribute XSS), CWE-84 (URI Scheme XSS), CWE-85 (Doubled Character XSS), CWE-86 (Invalid Character XSS), CWE-87 (Alternate XSS Syntax), or related XSS findings.

nosql-injection-testing
263

Validate NoSQL injection vulnerabilities across MongoDB, Cassandra, CouchDB, Redis, and other NoSQL databases. Test operator injection, JavaScript injection, and query manipulation patterns. Use when testing CWE-943 (Improper Neutralization of Special Elements in Data Query Logic) and related NoSQL injection classes.

command-injection-testing
263

Validate OS Command Injection vulnerabilities including direct command injection, blind command injection via time delays, and out-of-band command execution. Test by injecting shell metacharacters and commands into user-controlled inputs. Use when testing CWE-78 (OS Command Injection), CWE-77 (Command Injection), CWE-88 (Argument Injection), or related command execution vulnerabilities.