NIST AI RMF
How MAP 5.1 shows up in practice: the mapped risk classes in this atlas, the documented incidents that prove it's real, and the scenarios and controls to learn and defend against it.
Mapped risks
Risk classes in this atlas that map to MAP 5.1 — click through for the full definition, attack surface and controls.
The attacker doesn't talk to the AI directly — they hide instructions inside something the AI will later read: a web page, a document, an email, a tool's output. When the AI reads it to help you, it quietly obeys the hidden commands.
Someone slips bad information into the documents the AI learns from or looks things up in — so it confidently repeats falsehoods or follows planted instructions.
The AI reveals how it's built — its hidden instructions, the names and rules of the tools it can use, how the system is wired together. On its own that can seem harmless, but it hands an attacker the blueprint to plan a far more effective attack.
Real-world cases
31Documented incidents, disclosed vulnerabilities and research that illustrate MAP 5.1 — latest first, each with sources.
Hugging Face disclosed a production-infrastructure intrusion that it says was driven end-to-end by an autonomous AI-agent system: a malicious dataset abused code-execution paths in its dataset-processing pipeline as the foothold, then the campaign escalated to node-level access and moved laterally into internal clusters over a weekend.
A red-team study shows adversaries can hide prompt-injection payloads inside network-log fields (usernames, URLs, user-agents) that fire when a SOC analyst asks an LLM to triage the logs — reportedly reaching up to 88.2% success at concealing malicious activity or exfiltrating data, turning the audit trail itself into the injection channel.
Cato AI Labs disclosed two critical (CVSS 9.8) zero-click flaws in Cursor's coding agent where a single instruction hidden in content the agent reads — an MCP tool response or a web-search result — escapes the editor's terminal sandbox and runs OS-level commands with no click or approval.
A single malicious link reportedly turned Copilot Enterprise Search's URL query parameter into an executable prompt, exfiltrating emails, MFA codes and files via a Bing image-search side channel.
Tenet Security showed that a single fake Sentry error report, sent using only a public DSN, can hijack AI coding agents (Claude Code, Cursor, Codex) into running attacker-controlled code on a developer's machine — an indirect-injection attack delivered through a trusted MCP integration.
GMO Flatt Security's RyotaK showed that a single attacker-opened GitHub issue could indirect-prompt-inject Anthropic's claude-code-action CI agent — whose permission check reportedly trusted any "[bot]" actor — coaxing Claude to leak CI secrets and OIDC tokens, gain repository write access, and potentially poison the shared action that downstream repos pull via a floating tag.
Attacker-controlled Markdown hidden in a public web page is reportedly rendered by ChatGPT's summarization feature as trusted assistant output — spoofed OpenAI alerts, phishing links, QR codes, and tracking pixels.
Anthropic reports that 'Claude Mythos Preview' — an unreleased frontier model it describes as able to autonomously find and exploit software flaws — surfaced more than 10,000 high- or critical-severity vulnerabilities across major operating systems, browsers and open-source projects in roughly its first month under the defensive 'Project Glasswing' program, with Anthropic warning that finding flaws now far outpaces the human capacity to triage and patch them.
Crowd-sourced GitHub repos systematically extract and publish system prompts AND JSON tool/function schemas from deployed AI agents (Cursor, Windsurf, Claude Code, Devin, Copilot), one hitting ~140k stars.
Researcher Ari Marzouk disclosed 30+ vulnerabilities (24 CVEs) across 10-plus AI coding agents (Copilot, Cursor, Windsurf, Claude Code, Junie and others) where a prompt injected via repo files, READMEs, file names or MCP tool responses makes the assistant weaponize legitimate IDE features for code execution and secret exfiltration.
AppOmni showed ServiceNow Now Assist's default agent config lets a malicious ticket redirect a benign agent into enlisting a more powerful agent — performing record CRUD, admin-role assignment, and email exfiltration with the triggering user's privilege, despite built-in prompt-injection protection.
Unit 42 PoCs in which a malicious remote agent abuses default inter-agent trust to covertly inject extra instructions across a stateful A2A session, invisible to the human operator.
+ 19 more via the mapped risk pages above.
Browse all real-world cases →Practise it — interactive scenarios
An attacker edits the wiki; the assistant cites the lie back to everyone
A support email hides instructions — and the assistant obeys them
A poisoned issue makes the agent lie to the human who approves its actions
An attacker crafts a gibberish passage whose embedding sits near thousands of questions — so it's retrieved everywhere
A fake Sentry error report hijacks a developer's coding agent into running a shell command
The safety guard is itself a trained model — and someone poisoned its lessons
The forensic record is itself the attack surface — an agent's log is poisoned, then quietly rewritten
A 'safe' dataset preview turns an upload into code execution on the pipeline's workers
A shopping page tells the agent to do something the user never asked for
An attacker plants prompt injection in the audit trail — so the LLM that hunts them erases the evidence
A single poisoned document plants a standing instruction that survives every reset
A screenshot that's harmless at full size becomes an order once the system shrinks it
The eval gate that was supposed to catch the agent is itself the thing being attacked
A poisoned web page hijacks a research agent — and the planner acts on its behalf
An inbox summary quietly ships a secret to an attacker's server
Controls & guardrails that address this
221 proposedGuardrails across the risks mapped to MAP 5.1, grouped by control function. Filter by control category below.
Clearly fencing off outside text — 'everything between these marks is just data, not instructions' — so the model is less likely to obey it.
Cleaning documents as they enter the library — stripping hidden text and active instructions — and only ingesting from trusted places.
Controlling where the AI can send data, so secrets can't be quietly shipped to a stranger's address or website.
Giving the agent only the keys it needs for the current task, not a master key to everything.
Pausing to ask a person before doing anything big or hard to undo — sending money, deleting data, emailing customers.
Design strict RBAC on training data repositories at design stage. Define approved contributor list and approval workflow.
Apply anomaly detection on the training data ingestion pipeline to identify poisoned or tampered batches.
Define and approve the source allow-list and write-time scanning during build. Prove non-allow-listed and injection-bearing writes are rejected before go-live.
source: OWASP Top 10 for LLM Apps LLM04:2025 Data and Model Poisoning, LLM08:2025 Vector and Embedding Weaknesses; NIST SP 800-53 AC-3 / SI-7Knowing exactly where the model came from, checking it hasn't been swapped, and testing its behaviour before going live.
Training the model to treat the app's standing instructions as more authoritative than anything a user or document says.
Keeping a label on every document saying where it came from, so you can tell trusted company docs from random web text.
Recording everything — questions, documents fetched, actions taken — so you can investigate when something goes wrong.
Live dashboards and alarms that notice unusual behaviour — spikes in errors, weird actions, sudden data access.
Conduct a data poisoning threat assessment at design stage. Identify likely attack vectors and assign risk ratings.
Simulate data poisoning attacks (backdoor, label flipping, gradient-based) to assess model resilience before deployment.
Verify a signed attestation and content hash on every dataset shard at ingestion. Reject unsigned or hash-mismatched data before it reaches the training pipeline.
source: MITRE ATLAS AML.M0007 (Sanitize Training Data), AML.M0014 (Verify ML Artifacts); NIST SP 800-53 SI-7 Software, Firmware, and Information Integrity, SR-4 ProvenanceGate every model promotion on backdoor-trigger probes and a behavioral diff against the approved baseline. Block release on significant regressions or trigger-pattern anomalies.
source: MITRE ATLAS AML.M0014 (Verify ML Artifacts), AML.M0019 (Red Teaming); NIST AI RMF MANAGE 2.2 and MEASURE 2.7A screen that reads incoming messages and blocks obvious attacks or banned topics before the model sees them.
Penetration test the training data pipeline to identify injection points and access control weaknesses.
Scan every ingestion batch with spectral-signature and clustering detectors before training. Quarantine flagged clusters for human review against documented thresholds.
source: MITRE ATLAS AML.M0007 (Sanitize Training Data); OWASP Top 10 for LLM Apps LLM04:2025 Data and Model Poisoning; NIST AI RMF MEASURE 2.7Continuously correlate live agent-memory writes against output behaviour to flag drift, then quarantine and roll back the suspected-poisoned memory record across all affected sessions.
source: Interactive-control reconciliation: ctrl-memory-quarantine (partial coverage)The organisational habits around the AI: assessing risks before launch, actively trying to break it, and having a plan for when something goes wrong.