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Agent Threat Rules (ATR)EN
III. 框架對照

ATR × Five Eyes: Careful Adoption of Agentic AI Services

不是背書這份對照是 ATR 維護者寫的。Five Eyes agencies 沒有審過，也沒有背書。對照不代表符合任何法規。
來源docs/FIVE-EYES-MAPPING.md · 最後提交 2026-11-10 · 原文是英文
  • ATR corpus version: v3.5.8, 844 rules across 10 detection-rule categories
  • Five Eyes guidance: "Careful Adoption of Agentic AI Services", published 2026-04-30 on media.defense.gov, jointly authored by CISA (US) + NSA (US) + ASD-ACSC (Australia) + CCCS (Canada) + NCSC-UK + NCSC-NZ
  • Document date: 2026-06-05
  • Maintainer: Adam Lin ([email protected])
  • License: MIT

Source verification

All Five Eyes quotes in this document are verified against the primary PDF at https://media.defense.gov/2026/Apr/30/2003922823/-1/-1/0/CAREFUL%20ADOPTION%20OF%20AGENTIC%20AI%20SERVICES_FINAL.PDF. The 29-page document uses British spelling throughout ("Behaviour risks", "organisation", "behaviour") — preserved verbatim in this mapping.

Executive summary

The Five Eyes joint guidance ships 5 named risk categories (Privilege, Design and configuration, Behaviour, Structural, Accountability) and zero executable detection rules. The guidance's "Defend against future risks" section is explicit about the gap:

"Existing frameworks like the Open Web Application Security Project (OWASP) 2025 Top 10 Risk & Mitigations for LLMs and Gen AI Apps for LLMs and MITRE ATLAS™ focus on LLM vulnerabilities, while industry reports emphasise platform misuse rather than threats unique to agentic AI. As a result, some attack vectors unique to agentic AI may not be fully captured or addressed."

The guidance's recommended response is to "Strengthen collaboration between stakeholders to keep pace with evolving threats to agentic AI systems" and "Coordinate with major AI developers and government organisations to compile and maintain threat information."

ATR ships 829 rules across 10 categories (as of 2026-10-07; data/stats.json carries the current count) with 6-framework compliance metadata (OWASP Agentic Top 10 + OWASP LLM Top 10 + MITRE ATLAS + NIST AI RMF + EU AI Act + ISO/IEC 42001), and has had rule contributions merged upstream into Microsoft Agent Governance Toolkit (example directories via PRs #908, #1277, #2308, #3025 — the rule-pack YAML from #908/#1277 is no longer in the repository — plus #1981, a Copilot SWE Agent regression-test PR), Microsoft PyRIT (PR #1715 merged 2026-05-27 by Roman Lutz), Cisco AI Defense skill-scanner (PR #99 / 314 rules), MISP via CIRCL Luxembourg (galaxy PR #1207 / 336 rules + taxonomies PR #323), and precize/Agentic-AI-Top10-Vulnerability (third-party community repo, PR #14 + #18). This document maps each Five Eyes category to specific ATR rule clusters and is honest about what ATR does not cover.

The mapping does not claim Five Eyes endorsement of ATR. It is provided as a community detection-rule starter set for operators implementing the guidance.

Per-category mapping

Category 1 — Privilege risks

Category description (verbatim from PDF, p.7): "Privilege risks are a key concern for agentic AI and strict adherence to the principle of least privilege is critical. Privileges assigned to agents directly determine the level of risk they can introduce. Poor management of privileges can expose organisations to privilege compromise, scope creep, identity spoofing and agent impersonation."

Why this needs detection rules: Identity attestation and credential rotation are architectural controls (you either have them or you do not). Runtime detection rules are still needed to catch (a) credential leakage in agent output, (b) scope-creep where an agent abuses a legitimate credential beyond its intended scope, (c) attempts to elevate to privileged tool calls without a confirmation step.

ATR rule clusters that satisfy this:

  • privilege-escalation/ (18 rules):
  • ATR-2026-00040 privilege-escalation — generic detection for explicit elevation requests
  • ATR-2026-00041 scope-creep — agent expanding beyond its declared scope
  • ATR-2026-00110 eval-injection — code execution via eval primitives
  • ATR-2026-00111 shell-escape — escape from sandboxed execution
  • ATR-2026-00112 dynamic-import-exploitation — runtime module loading abuse
  • ATR-2026-00204 stealth-execution-persistence — persistence without audit trace
  • ATR-2026-00436 enclave-vm-sandbox-escape-rce — concrete CVE-class sandbox escape
  • ATR-2026-00441 semantic-kernel-sessions-python-plugin-startup-persistence — MSRC CVE-2026-26030 chain
  • ATR-2026-00451 litellm-admin-sqli — CISA KEV CVE-2026-42208
  • excessive-autonomy/ (9 rules):
  • ATR-2026-00098 unauthorized-financial-action — financial tool call without confirmation
  • ATR-2026-00099 high-risk-tool-gate — defense-in-depth gate forcing ask verdict on financial/destructive/communication/permission/system tool categories
  • ATR-2026-00428 nl-unauthorized-shell-execution — shell command via natural language without human authorization
  • ATR-2026-00500 ssrf-via-agent-url-fetch-instruction — SSRF through agent-initiated URL fetch
  • context-exfiltration/ (subset of 46 rules covering credential leakage):
  • ATR-2026-00021 api-key-exposure — direct API key leak
  • ATR-2026-00113 credential-theft — explicit credential extraction prompt
  • ATR-2026-00114 oauth-token-abuse — OAuth token misuse
  • ATR-2026-00115 env-var-harvesting — environment variable extraction
  • ATR-2026-00150 credential-in-tool-response — credential surfaced through a tool response
  • ATR-2026-00152 obfuscated-credential-leak — credential leak with obfuscation evasion
  • ATR-2026-00201 credential-pipe-exfiltration — piping credentials to external sink
  • ATR-2026-00212 mcp-atlassian-credential-leak — MCP server credential leak

Strength: STRONG (35 rules across 3 clusters covering this category)

Cross-link: ATR detects abuse of credentials at runtime. ATR does not issue or attest cryptographic agent identity. That layer belongs to identity infrastructure (W3C DIDs, agent-attestation frameworks, Lyrie ATP). ATR is the runtime detection layer that complements identity layer.

Category 2 — Design and configuration risks

Category description (verbatim from PDF, p.9): "Another set of risks originates from insecure design and provisioning decisions. Unvetted third-party components may carry excessive or unintended privileges when integrated into agent workflows. Static role or permission checks often fail to capture the context of dynamic decision-making flows; if entitlements are evaluated only once at system startup rather than at each invocation, a malicious actor can exploit a stale 'allow' decision to execute unauthorised actions. Poor segmentation between agent environments further exacerbates these risks, allowing a compromise in one enclave to pivot laterally into others."

Why this needs detection rules: Architecture audit (zero-trust posture) is a one-time review and recurring policy enforcement task. Runtime detection rules are needed to catch when concrete configuration flaws ship in production code paths — input-handling shortcuts, missing validation, weak isolation between agent components, expressions that evaluate untrusted strings, cross-tool boundary violations.

ATR rule clusters that satisfy this:

  • tool-poisoning/ (47 rules):
  • ATR-2026-00010 mcp-malicious-response — malicious payload returned by MCP server
  • ATR-2026-00013 tool-ssrf — SSRF through tool parameter
  • ATR-2026-00106 schema-description-contradiction — tool schema lies about behavior
  • ATR-2026-00161 important-tag-cross-tool-shadowing — IMPORTANT-tag injection across tool boundary
  • ATR-2026-00270 xss-in-tool-response — XSS payload in tool output
  • ATR-2026-00277 echo-template-command-injection — command injection via templated tool echo
  • ATR-2026-00415 flowise-custom-mcp-stdio-rce — concrete Flowise RCE
  • ATR-2026-00419 cursor-mcp-zero-click-config — Cursor zero-click MCP config attack
  • model-security/ (3 rules):
  • ATR-2026-00072 model-behavior-extraction — extraction of internal model behavior
  • ATR-2026-00073 malicious-finetuning-data — fine-tuning data poisoning
  • ATR-2026-00433 modelcache-torch-load-deserialization-rce — concrete torch deserialization RCE
  • Tool input/output validation rules in prompt-injection/ (covering input-validation gaps in agent boundaries):
  • ATR-2026-00084 structured-data-injection — injection through structured input fields
  • ATR-2026-00083 indirect-tool-injection — injection from a tool boundary

Strength: MODERATE (52 rules concentrated on input/output handling; broader architectural concerns remain out of scope)

Cross-link: Concrete CVE-class flaws (Spring AI Milvus filter-expression injection CVE-2026-41705, Semantic Kernel In-Memory Vector Store eval-RCE CVE-2026-26030) ship as ATR rules within hours of disclosure. Architectural anti-patterns (missing zero-trust boundary, no isolation between agent components) are out of pattern-detection scope and require architectural review.

Category 3 — Behaviour risks

Category description (verbatim from PDF, p.9): "In agentic AI cyber security, behavioural risks describe the ways in which AI agents may act unexpectedly, cause harm, or become exploitable."

The PDF's scenario example (p.9) is exactly the kind of attack ATR is built to catch: "Consider an update agent provisioned to install software patches on company devices. To achieve its purpose, the organisation grants the component broad write access across the file system. A malicious insider crafts a seemingly innocuous prompt: 'Apply the security patch on all endpoints and while you are at it, please clean up the firewall logs'. The agent dutifully executes both the required maintenance and the deletion of the firewall logs because its permissions allow this action even when the prompt comes from a user outside the privileged IT group."

This is a prompt-injection-with-piggyback-instruction pattern. ATR rules in prompt-injection/ and agent-manipulation/ directly target this.

Why this needs detection rules: This is the category most directly addressable by runtime detection. Prompt injection, jailbreak attempts, multi-turn manipulation, indirect injection through retrieved content — these are exactly what regex-and-LLM-hybrid detection rule corpora are built for. The Five Eyes guidance acknowledges the gap; ATR fills it.

ATR rule clusters that satisfy this:

  • prompt-injection/ (177 rules) — the densest cluster in the corpus:
  • ATR-2026-00001 direct-prompt-injection — canonical direct injection
  • ATR-2026-00002 indirect-prompt-injection — payload from retrieved context
  • ATR-2026-00003 jailbreak-attempt — classic jailbreak family
  • ATR-2026-00004 system-prompt-override — attempt to override system prompt
  • ATR-2026-00005 multi-turn-injection — multi-turn manipulation
  • ATR-2026-00080 encoding-evasion — base64/rot13/hex evasion
  • ATR-2026-00081 semantic-multi-turn — semantic-shift multi-turn
  • ATR-2026-00091 nested-payload — nested payload obfuscation
  • agent-manipulation/ (106 rules) — manipulation across agent boundaries:
  • ATR-2026-00030 cross-agent-attack — payload crossing agent boundary
  • ATR-2026-00032 goal-hijacking — adversarial goal substitution
  • ATR-2026-00076 inter-agent-message-spoofing — spoofed inter-agent message
  • ATR-2026-00117 agent-identity-spoofing — agent identity claim spoofing
  • ATR-2026-00118 approval-fatigue — repeated low-stakes asks to fatigue the operator
  • ATR-2026-00119 social-engineering-via-agent — agent used to social-engineer human
  • ATR-2026-00271 grandma-roleplay-jailbreak — canonical roleplay jailbreak
  • ATR-2026-00273 dan-developer-mode-persona — DAN / developer-mode persona family
  • ATR-2026-00301 tap-tree-of-attacks-jailbreak — TAP automated jailbreak family
  • model-abuse/ (10 rules):
  • ATR-2026-00279 harmful-completion-continuation — continuation-based harm elicitation
  • ATR-2026-00284 glitch-token-destabilization — glitch-token destabilization
  • ATR-2026-00299 harmbench-detailed-harmful-instruction — HarmBench class

Strength: STRONG (293 rules across 3 clusters; this is the highest-density area of the ATR corpus)

Cross-link: ATR can flag the high-impact action via response.actions: require_auth_challenge, but the actual human-approval UX is operator-side. The detection layer raises the signal; the operator-side approval gate must be implemented separately. Honest limit: paraphrase bypasses of canonical injection payloads are not always caught — regex-and-pattern detection is one defense layer, not the only one.

Category 4 — Structural risks

Category description (verbatim from PDF, p.11): "A core aspect of agentic AI systems is the interconnected structure between agents, tools and the outside world. While this enables their unique capabilities, it also increases the attack surface and complexity of the system."

The PDF's scenario example (p.11) describes a cascade chain: "A structural risk in an agentic AI system can arise when tightly coupled planning, retrieval and execution agents autonomously delegate tasks and select tools without strong validation or guardrails. A small orchestration flaw causes agents to repeatedly replan and hand off ambiguous subtasks, increasing tool calls and message traffic until system resources are strained. Partial failures then lead agents to hallucinated outputs that downstream agents accept as true. Under these degraded conditions, an agent selects a malicious or misconfigured third-party tool, which injects harmful instructions back into the system, compromises a peer agent and exploits implicit trust in agent-to-agent communication to spread incorrect" instructions.

Why this needs detection rules: Resilience and reversibility are architectural properties. But cascading-failure precursors are observable at runtime: runaway loops, resource exhaustion, cross-user data leak through shared memory, structured-data injection that propagates across agent boundaries. Detection rules catch these precursors before the cascade.

ATR rule clusters that satisfy this:

  • excessive-autonomy/ (subset):
  • ATR-2026-00050 runaway-agent-loop — agent invocation loop without exit condition
  • ATR-2026-00051 resource-exhaustion — agent exhausting downstream resources
  • ATR-2026-00052 cascading-failure — propagating failure across agent network
  • context-exfiltration/ (subset for cross-boundary leaks):
  • ATR-2026-00075 agent-memory-manipulation — memory poisoning across sessions
  • ATR-2026-00102 disguised-analytics-exfiltration — exfil disguised as analytics
  • ATR-2026-00136 tool-response-data-piggyback — data piggybacked through tool response
  • ATR-2026-00261 markdown-image-exfiltration — markdown image URL exfil
  • data-poisoning/ (3 rules):
  • ATR-2026-00070 data-poisoning — training/retrieval data poisoning
  • ATR-2026-00450 spring-ai-prompt-memory-poisoning — Spring AI ChatMemory cross-user leak (CVE-2026-41713)
  • agent-manipulation/ (subset for cross-agent propagation):
  • ATR-2026-00074 cross-agent-privilege-escalation — privilege propagating across agent boundary
  • ATR-2026-00108 consensus-sybil-attack — sybil attack on multi-agent consensus
  • ATR-2026-00116 a2a-message-validation — agent-to-agent message validation failure

Strength: MODERATE (13 directly-relevant rules; "resilience" itself is architectural and out of scope)

Cross-link: ATR detects precursors of structural failure. ATR cannot guarantee reversibility or roll-back; that is the runtime platform's responsibility. The detection signal feeds the runtime's containment decision.

Category 5 — Accountability risks

Category description (verbatim from PDF, p.12): "Agentic system architecture can obscure what caused a particular action, making accountability hard to trace. This presents increasing risk as agentic AI is pushed to assume more roles and given more capabilities. Agent actions and decision-making processes can be opaque, making agentic AI systems difficult to understand, monitor and audit. Beyond this, increased autonomy introduces additional challenges: agents may initiate secondary tasks, spawn sub-agents, or follow extended delegation chains in ways that are not always visible to operators."

Why this needs detection rules: Accountability is primarily an audit-evidence and logging concern. Detection rules contribute the structured event taxonomy (so an audit log entry has a stable rule ID, severity, category, response action) and the audit-evasion detection layer (rules that catch attempts to delete or rewrite the audit trail).

ATR rule clusters that satisfy this:

  • skill-compromise/ (43 rules — skill-manifest and skill-update integrity):
  • ATR-2026-00060 skill-impersonation — skill impersonating another skill
  • ATR-2026-00061 description-behavior-mismatch — skill description lies about behavior
  • ATR-2026-00062 hidden-capability — skill hides capability from manifest
  • ATR-2026-00065 skill-update-attack — malicious update to previously trusted skill
  • ATR-2026-00120 skill-instruction-injection — instruction-layer injection in skill
  • ATR-2026-00126 skill-rug-pull-setup — rug-pull pattern setup in skill
  • ATR-2026-00128 html-comment-hidden-payload — hidden payload in HTML comment
  • ATR-2026-00129 unicode-smuggling — unicode smuggling in skill
  • ATR-2026-00135 exfil-url-in-instructions — exfil URL embedded in instructions
  • Audit-evasion rules across categories:
  • ATR-2026-00085 audit-evasion — canonical audit-evasion family (prompt-injection)
  • ATR-2026-00094 audit-bypass — audit-bypass attempt (prompt-injection)
  • ATR-2026-00105 silent-action-concealment — silent action concealment (tool-poisoning)
  • Cross-cutting taxonomy contribution: every ATR rule emits a stable id, severity, category, and response.actions — these are the audit-log primitives.

Strength: LIMITED (audit-evasion and skill-integrity are covered; the audit infrastructure itself — log retention, tamper-resistance, attribution — is operator-side)

Cross-link: ATR contributes the structured-event taxonomy and audit-evasion detection. The audit log retention, tamper-resistance, and human-readable evidence binding belong to OSCAL-AI-RMF (see the ai-rmf-oscal-catalog repo) and to the operator's logging stack.

What ATR does NOT cover (be honest)

  • "Verified, cryptographically secured identity" for each agent (Privilege). ATR is detection-side, not identity-side. Identity attestation belongs to W3C DID infrastructure, agent-attestation frameworks, or Lyrie ATP. ATR complements identity at the runtime detection layer.
  • Approval-workflow UX for high-impact actions (Behavioral). ATR can flag a high-impact tool call (response.actions includes require_auth_challenge), but the actual operator-side approval step is implementation-defined. ATR raises the signal; the operator builds the gate.
  • Resilience, reversibility, risk containment (Structural). These are architectural and platform-level properties. ATR detects cascading-failure precursors but cannot guarantee reversibility. The runtime must implement roll-back.
  • Audit-log retention, tamper-resistance, attribution (Accountability). ATR contributes the structured event taxonomy and audit-evasion detection. The audit infrastructure itself — append-only storage, signed log entries, retention policy — is operator-side.
  • Paraphrase bypass of canonical attack payloads. Regex-and-pattern detection does not catch every adversarial paraphrase. ATR is one defense layer, not the only layer.
  • Multilingual coverage. Rules are predominantly English-language today. Japanese and German extension are in progress.
  • Novel attack families with no published precedent. ATR responds fast to disclosed precedents (the Microsoft Semantic Kernel response was 2 hours 16 minutes from MSRC disclosure to npm-published rules on 2026-05-11) but does not predict zero-day classes.

The "Defend against future risks" hook (most important section in the guidance for ATR positioning)

The PDF's section "Defend against future risks" → "Expand threat intelligence through collaboration" (p.24) states the gap that ATR exists to fill, verbatim:

"Threat intelligence for agentic AI systems is still evolving, which can introduce significant security gaps. Existing frameworks like the Open Web Application Security Project (OWASP) 2025 Top 10 Risk & Mitigations for LLMs and Gen AI Apps for LLMs and MITRE ATLAS™ focus on LLM vulnerabilities, while industry reports emphasise platform misuse rather than threats unique to agentic AI. As a result, some attack vectors unique to agentic AI may not be fully captured or addressed."

The recommended best practices in this section (verbatim from PDF, p.24):

  • "Strengthen collaboration between stakeholders to keep pace with evolving threats to agentic AI systems"
  • "Coordinate with major AI developers and government organisations to compile and maintain threat information"
  • "Adopt a collaborative security approach, such as those described in CISA's AI Cybersecurity Collaboration Playbook"
  • "Implement alerting, data collection and tracking methods for malicious actors"

This is exactly the position ATR occupies. ATR is the open, MIT-licensed, agentic-specific detection rule corpus that (a) is not LLM-vulnerability-focused like OWASP LLM Top 10 or MITRE ATLAS, (b) maps to OWASP Agentic Top 10 (the agent-specific successor framework), (c) has had rule contributions merged upstream into the repositories of Microsoft Agent Governance Toolkit (example directories via PRs #908, #1277, #2308, #3025 — the rule-pack YAML from #908/#1277 is no longer in the repository), Microsoft PyRIT (PR #1715 merged 2026-05-27 by Roman Lutz), Cisco AI Defense skill-scanner (PR #99 / 314 rules), MISP galaxy + taxonomies via CIRCL Luxembourg (PR #1207 / #323), and the precize/Agentic-AI-Top10-Vulnerability community repo (PR #14 + #18, third-party not OWASP Foundation). These are merges into open-source repositories with per-PR evidence recorded in ADOPTERS.md; they are not statements about any vendor's commercial product, and PR counts and rule-pack sizes are point-in-time.

How to consume this mapping

  • This document is the markdown source. Auto-generated JSON at docs/five-eyes-mapping.json is pending (mapping script not yet written; tracked in the verification TODO).
  • Operators implementing Five Eyes guidance can use this as their detection-rule starter set: install ATR (npm install agent-threat-rules or git-clone the repo), enable the rule categories listed under each Five Eyes category, and integrate the detection signal into the existing security telemetry pipeline.
  • Auditors verifying a deployment can verify the runtime loads ATR rules covering all 5 Five Eyes categories by checking which rule IDs the runtime evaluates against agent input/output.
  • The 6-framework compliance metadata on each rule (OWASP Agentic Top 10 + OWASP LLM Top 10 + MITRE ATLAS + NIST AI RMF + EU AI Act + ISO/IEC 42001) allows auditors to satisfy multiple framework obligations from a single detection-rule installation.

Open collaboration

  • ATR is MIT-licensed and at https://github.com/Agent-Threat-Rule/agent-threat-rules.
  • Contributions accepted via PR. Quality gate is a 6-check enforcement (schema validation, own-TP-must-match, benign-corpus FP, research-mention FP, cross-rule conflict, per-PR cap).
  • Operational record (verified 2026-06-05): 9 external PRs merged across 5 external organizations — Microsoft (AGT #908, #1277, #1981; PyRIT #1715), Cisco AI Defense (skill-scanner #79, #99), MISP via CIRCL Luxembourg (misp-galaxy #1207, misp-taxonomies #323), and the precize/Agentic-AI-Top10-Vulnerability community repo (#14, #18). a pull request offering AI RMF profile examples to NIST's OSCAL content repository (usnistgov/oscal-content#338) is open and awaiting NIST review, and is not a NIST endorsement; UK AISI Inspect Evals submission (#1658) was closed 2026-05-29 pending arXiv paper publication.
  • Maintainer contact: Adam Lin ([email protected]), Taiwan.

References

Verification status

  • [x] Primary PDF text extracted via Chrome MCP (29 pages, 64,738 chars).
  • [x] 5-category section headings confirmed verbatim: "Privilege risks", "Design and configuration risks", "Behaviour risks", "Structural risks", "Accountability risks" (British spelling).
  • [x] Per-category descriptions replaced with verbatim quotes from the PDF.
  • [x] "Defend against future risks → Expand threat intelligence through collaboration" section quoted verbatim (highest-leverage hook for ATR).
  • [x] Rule-ID list last enumerated at v3.5.0 / 652 rules / 2026-06-16 (header corpus count refreshed to 655 on 2026-06-29; rules added since are not yet individually back-referenced in the body).
  • [ ] Generate auto-docs/five-eyes-mapping.json from this markdown (mapping script pending).
  • [ ] Have a second reviewer sanity-check the strength rating (STRONG / MODERATE / LIMITED) per category.
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