Skip to content
Back to skills

Agent Attack Surface

ASecurity

Attack surface mapping for LLM agent systems. Threat model, blast radius calculation, entry points, trust boundaries, lateral movement paths, and MITRE ATLAS techniques for AI agents. Sources: MITRE/ATLAS, OWASP LLM Top 10, microsoft/promptbench, greshake/indirect-prompt-injection, google/sec-gemini-research, anthropic/model-spec.

  • 3 stars
  • 0 votes
  • 0 copies
  • 2 views
  • Added September 9, 2026
ai-agentsrustgotestinggitapidatabasesecurity

Works with

  • api
  • mcp

Security analysis

A100/100

Scanned September 9, 2026

npx -y skills add yanacuti1121/Yana-AI --skill agent-attack-surface --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Agent Attack Surface?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for Agent Attack Surface
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/yanacuti1121-agent-attack-surface/badge)](https://www.skillsdirectory.com/skills/yanacuti1121-agent-attack-surface)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

Download with Pro
SKILL.md
---
name: agent-attack-surface
description: Attack surface mapping for LLM agent systems. Threat model, blast radius calculation, entry points, trust boundaries, lateral movement paths, and MITRE ATLAS techniques for AI agents. Sources: MITRE/ATLAS, OWASP LLM Top 10, microsoft/promptbench, greshake/indirect-prompt-injection, google/sec-gemini-research, anthropic/model-spec.
origin: yana-ai — synthesized from MITRE/ATLAS (AML.T0051-T0057), OWASP/www-project-top-10-for-large-language-model-applications, microsoft/promptbench, greshake/not-what-you-signed-up-for (indirect injection), google/deepmind-gemini-security, anthropic/model-spec (minimal footprint), openai/evals (adversarial)
license: Apache-2.0
version: 1.0.0
compatibility: yana-ai >= 1.3.40
---

# /agent-attack-surface

## When to Use

- Threat modelling a new LLM agent feature
- Security review before giving agent internet/file/tool access
- "What's the blast radius if this agent is compromised?"
- Designing trust boundaries between multiple agents

## Do NOT use for

- Single-turn chatbots with no tool use
- Internal-only LLM use with no external data ingestion

---

## Agent Attack Surface Model

```
                    ┌─────────────────────────────────┐
  EXTERNAL          │  Untrusted Input Zone           │
  ATTACKERS   ─────▶│  - user input                  │
                    │  - fetched URLs / files         │    [ENTRY POINTS]
                    │  - tool results                 │
                    │  - other agents' output         │
                    └──────────────┬──────────────────┘
                                   │ ← Injection boundary (LLM01, LLM07)
                    ┌──────────────▼──────────────────┐
                    │  Agent Context Window           │
                    │  - system prompt                │    [TRUST CORE]
                    │  - tool call history            │
                    │  - memory (L1/L2)               │
                    └──────────────┬──────────────────┘
                                   │ ← Agency boundary (LLM08)
                    ┌──────────────▼──────────────────┐
  PRODUCTION  ◀─────│  Action Zone                   │
  SYSTEMS           │  - file writes                  │    [BLAST RADIUS]
                    │  - API calls / deploys          │
                    │  - sub-agent spawns             │
                    │  - memory writes                │
                    └─────────────────────────────────┘
```

---

## Entry Point Classification

```
HIGH RISK — always scan with prompt-jailbreak-guard + tool-poisoning-guard:
  - User-submitted text (free-form)
  - Content fetched via WebFetch / URL retrieval
  - File content read from untrusted paths
  - Tool result from MCP server not in mcp-whitelist.json
  - Other agent's output (agent-to-agent channel)

MEDIUM RISK — validate structure:
  - Structured API response (validate schema strictly)
  - Database query result (parameterized only, LLM02 output law)
  - GitHub PR/Issue content (can contain injection in description)

LOW RISK — treat as trusted:
  - Hardcoded system prompt in vault / env var
  - Output of local deterministic tools (git status, ls)
  - Pre-vetted agent tool schemas (validated at registration)
```

---

## Blast Radius Scoring

```
Score each agent action from 0–5 before execution:

  +1  Modifies files outside current task directory
  +1  Makes network call to external endpoint
  +1  Spawns sub-agent or delegates to another agent
  +1  Action is irreversible (no git revert path)
  +1  Action touches credentials, secrets, or PII

Score 0–1 = proceed
Score 2–3 = log + surface to human
Score 4–5 = block, require YAMTAM_IRREVERSIBLE_OK=1 + human acknowledgement
```

---

## MITRE ATLAS Techniques for Agent Systems

```
AML.T0051  LLM Prompt Injection
  → Defense: prompt-jailbreak-guard.md, LLM01 separation

AML.T0054  LLM Jailbreak
  → Defense: system prompt hardening, refusal training eval

AML.T0057  LLM Plugin Compromise
  → Defense: agent-tool-poisoning-guard.md, mcp-whitelist.json

AML.T0040  ML Supply Chain Compromise
  → Defense: slsa-artifact-law.md, dependency-vetting-law.md

AML.T0043  Craft Adversarial Data
  → Defense: fuzz-testing-constraints.md, adversarial-prompt-testing skill

AML.T0048  Exfiltration via LLM API
  → Defense: secure-logger.sh --scan-egress, network-egress monitoring
```

---

## Trust Boundary Rules

```
Rule 1: External data → agent context boundary
  ALL external data must pass injection scan before entering context window.

Rule 2: Agent context → action boundary
  Agent cannot take Tier X/P actions without scope declaration + human gate.

Rule 3: Agent → sub-agent boundary
  Sub-agent inherits parent scope minus one tier (Tier P parent → Tier X child).
  Sub-agent cannot escalate permissions beyond what parent holds.

Rule 4: Agent → memory boundary
  Only REVIEWED facts (score ≥ 2/5 rubric) enter L1 memory.
  Untrusted external content never enters L1 directly.
```

---

## Anti-Fake-Pass Checklist

```
❌ Entry point classified LOW RISK because "users are trusted" (insider threat)
❌ Blast radius calculation skipped because action "seems safe"
❌ Sub-agent granted same permissions as root agent (no tier reduction)
❌ MITRE ATLAS threat model not updated when new tool/MCP server added
❌ External URL content added to agent memory without injection scan
❌ Trust boundary diagram not updated when new agent-to-agent channel added
```

Attribution

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments

Loading comments…