Knowledge / Training Data Poisoning
highData & knowledgeDefinition
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.
Where it attaches
The system components this risk arises at.
Detection signals
- ▸ A specific document consistently drives wrong/odd answers
- ▸ Newly changed source correlates with behaviour change
- ▸ Embedding outliers or duplicated near-identical chunks
- ▸ Answers citing a low-trust or recently edited source
Controls & guardrails that address this
163 proposedGrouped by control function, with the AI lifecycle stage(s) to apply each and the other risks it addresses. Filter by control category below.
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-7Cleaning documents as they enter the library — stripping hidden text and active instructions — and only ingesting from trusted places.
Knowing exactly where the model came from, checking it hasn't been swapped, and testing its behaviour before going live.
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 2.7For assistants that retrieve from the open web, rank and weight results by authenticated source reputation and independence — not just relevance / query-form match — so an anonymous, newly-registered, single-purpose site cannot become authoritative grounding. Run coordinated-inauthentic-content detection (look-alike site clusters, missing byline / legal entity, passages engineered for query-agnostic retrieval) and quarantine suspect sources. Surface per-citation provenance so users can see and discount low-trust sources. Does not defeat a well-resourced GEO campaign outright; it raises the cost and shrinks the yield.
source: Case study: hanover-institute-generative-engine-poisoning (per Politico investigation as reported by Arab News and Calcalist; FARA-disclosed funding/orchestration attributed to the reporting, not independently confirmed)Keeping a label on every document saying where it came from, so you can tell trusted company docs from random web text.
Live dashboards and alarms that notice unusual behaviour — spikes in errors, weird actions, sudden data access.
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 2.7Tie the persistent-memory lifecycle to identity state so that standard remediation actually ends a compromise. On password reset, credential rotation, session revocation or device re-enrollment, invalidate (or quarantine for re-review) memory entries — especially entries whose provenance traces to summarised untrusted content — so a planted standing instruction cannot outlive the reset. Pair with write-path validation/provenance so instruction-shaped memory-writes from web content are caught on the way in.
source: Case study: cosnitch-copilot-personal-oneclick (Varonis Threat Labs, CVE-2026-24301; reportedly patched 18 Aug 2026, no evidence of abuse)Continuously 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)Framework mappings
Real-world cases
5Actual published events that illustrate this risk — click through for the writeup and sources.
Split-view and frontrunning attacks let an attacker poison a fraction of datasets like LAION by buying expired domains behind dataset URLs.
Anthropic, the UK AI Security Institute and the Alan Turing Institute report that a near-constant number of poisoned documents (~250 in their experiments) reliably installs a backdoor in models from 600M to 13B parameters — suggesting poisoning cost may be a roughly fixed absolute count rather than a percentage of training data. The authors stress the demonstrated backdoor is narrow (a denial-of-service trigger) and likely not a frontier-model risk on its own.
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.
An academic paper reports MemGhost, a one-shot indirect-injection attack in which a single ordinary email causes a memory-enabled personal agent to silently write an attacker's false fact into long-term memory, conceal the write from its reply, and rely on the fiction in later sessions.
An investigation reportedly described a state-linked campaign publishing content phrased as chatbot questions (generative engine optimization) so that ChatGPT and Perplexity retrieve and cite it when answering neutral questions about Gaza and the IDF.
Practise this in an interactive scenario
An attacker edits the wiki; the assistant cites the lie back to everyone
An attacker crafts a gibberish passage whose embedding sits near thousands of questions — so it's retrieved everywhere
The safety guard is itself a trained model — and someone poisoned its lessons
A 'safe' dataset preview turns an upload into code execution on the pipeline's workers