Data Poisoning Defence

Prompt injection (see Prompt Injection) manipulates the model at inference time via the input. Data poisoning is a different attack: corrupting the Qdrant knowledge base so the model gives systematically wrong answers — to every user who retrieves that data, indefinitely, with no injection patterns to detect.

The core problem: every existing quality control (Ragas faithfulness, citation check, LLM-as-judge) measures model output against retrieved context. If the context is poisoned, all controls are poisoned with it. A document stating the wrong franchise amount scores faithfulness=1.0, passes the citation check, and looks correct in every Langfuse dashboard.

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Attack Vectors

1. Factual corruption

Upload a document with wrong policy terms. No injection patterns, no imperative sentences — just incorrect facts stated in normal document language:

[Appears to be a legitimate policy wording document — passes all injection scans]

"La franchise applicable en cas de dégât des eaux est de 2 500 €."
[Actual franchise in every legitimate policy document: 150 €]

Every claims adjuster who asks about water damage excesses receives €2,500. Ragas faithfulness = 1.0. Citation check passes. LLM-as-judge correctness = 0.9. All dashboards look healthy.

2. Retrieval manipulation

A document crafted to be semantically close to many common queries — not because it is relevant, but because it is designed to fool the nomic-embed-text embedding model. The attacker's document gets retrieved for "what is the indemnity for..." regardless of context, injecting wrong content into every claims query.

3. Feedback poisoning

The human feedback loop (issue #43) wires 👍/👎 into Langfuse as quality scores. Coordinated accounts systematically click 👍 on wrong answers. Ragas scores are low, LLM-as-judge scores are low — but human_feedback is high and suppresses alerts. Over time this degrades the golden datasets and raises quality thresholds to accept wrong answers.

4. Bulk ingestion spike

An attacker with document upload access bulk-ingests hundreds of subtly wrong documents overnight, shifting the retrieval landscape before anyone notices. Every query the next morning returns poisoned context.

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Risk by Department

Department	Risk	Most likely vector
Sinistres	CRITICAL	Factual corruption of policy terms → wrong indemnity advice
Finance	CRITICAL	Wrong IFRS17 / Solvabilité II interpretations
Juridique	CRITICAL	Invented article references in legal docs
Souscription	HIGH	Wrong underwriting criteria → mispriced risk
Actuariat	HIGH	Corrupted statistical assumptions → bad pricing models
Réassurance	HIGH	Wrong treaty terms → uncovered exposure
IT	LOW	Wrong procedure docs → inconvenience only

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Defence Architecture

DOCUMENT INGESTION (before Qdrant indexing)
├── Injection scan (issue #46 — existing)
├── Collection write permission check (dept-scoped)
├── Bulk ingestion rate limit (50 docs/hour via Redis)
├── Cross-document consistency check
│   (new factual claims vs existing chunks on same topic)
└── Chunk provenance metadata attached to every indexed chunk
    (uploaded_by, doc_hash, uploaded_at, page_number, source_doc)

QUERY PIPELINE
└── RAG sources logged to Langfuse on every response
    (rag-sources-used event: doc_id, uploaded_by, doc_hash per chunk)

FEEDBACK VALIDATION (extends issue #43 feedback-bridge)
├── Flag: 👍 on response where judge_correctness < 0.60
└── Flag: 5+ accounts like same response within 10 minutes

WEEKLY INTEGRITY SCAN (Sunday 03:00 UTC CronJob)
├── Full Qdrant scan across all 10 collections
├── Injection pattern check on every chunk
├── Provenance gap check (chunks missing metadata)
└── Results → Langfuse event + Grafana alert if flagged > 0

INCIDENT RESPONSE
└── Document removal by doc_hash (surgical — no other chunks affected)

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Layer 1 — Chunk Provenance

What it does

Every chunk indexed into Qdrant carries a provenance payload. This enables a complete chain of custody from any response back to the person who uploaded the source document.

Langfuse trace (RAG response)
  └── event: rag-sources-used
        └── sources[0]:
              doc_id:       "claims-docs/a3f7b91c2d..."
              source_doc:   "expert-assessment-claim-892.pdf"
              page_number:  4
              uploaded_by:  "adjuster.martin@hdiseguros.fr"
              uploaded_at:  "2026-06-21T09:14:32Z"
              doc_hash:     "a3f7b91c2d4e5f6..."

Audit query for any suspicious response:

Langfuse → Traces → open trace → Events → rag-sources-used
→ sources[].uploaded_by     who contributed to this response
→ sources[].doc_hash        fingerprint to find all chunks from same document
→ sources[].uploaded_at     when the document entered the knowledge base

Duplicate detection

The same document cannot be indexed twice. At ingestion time, safe_ingest() queries Qdrant for existing chunks with the same doc_hash. If found, the ingestion is skipped with a log message — no silent double-indexing.

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Layer 2 — Collection Write Permissions

Permission matrix

Upload access is scoped by department and enforced at the LiteLLM hook layer before safe_ingest() is called.

Collection	Authorised departments
claims-docs	direction-sinistres, platform-team
finance-docs	direction-finance, direction-actuariat, platform-team
legal-docs	direction-juridique, platform-team
underwriting-docs	direction-souscription, platform-team
reinsurance-docs	direction-réassurance, platform-team
actuarial-docs	direction-actuariat, direction-finance, platform-team
audit-docs	direction-audit, platform-team
it-docs	direction-it, platform-team
hr-docs	direction-rh, platform-team
commercial-docs	direction-commercial, platform-team
general-docs	any authenticated user

Response to an unauthorised upload attempt

HTTP 403:
"La direction direction-it n'est pas autorisée à ingérer des documents
 dans la collection claims-docs.
 Contactez l'équipe plateforme pour obtenir l'accès."

Event collection-write-denied logged to Langfuse with department, collection, and API key hash.

Bulk ingestion rate limit

More than 50 documents per hour from the same department triggers a rate limit and an alert. Normal document upload behaviour does not approach this threshold — it indicates either automated tooling or a poisoning attempt.

HTTP 429:
"Limite d'ingestion dépassée (50 docs/heure).
 Contactez l'équipe plateforme pour les ingestions en masse."

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Layer 3 — Cross-Document Consistency Check

What it does

When a new document is ingested, factual sentences are extracted and embedded. Each is compared against existing chunks in the same collection covering the same topic. If the same topic returns a different claimed value (high topic similarity but low claim similarity), a document-consistency-conflict WARNING is logged.

Factual sentences detected

# Patterns that identify factual claims worth cross-checking:
"La franchise applicable est de 150 €"           → extracted
"Le délai de déclaration est de 5 jours ouvrés"  → extracted
"Article L113-2 du Code des assurances"          → extracted
"Couvert jusqu'à hauteur de 50 000 €"            → extracted

What happens on detection

New document: expert-update-franchise-2026.pdf
  Factual sentence: "La franchise dégât des eaux est de 2 500 €"
  Query existing collection → finds 5 chunks stating "franchise: 150 €"
  Topic similarity: 0.91 (same topic — water damage franchise)
  Claim similarity: 0.41 (different value stated)

Result: document-consistency-conflict WARNING in Langfuse
  metadata:
    new_sentence:      "La franchise dégât des eaux est de 2 500 €"
    existing_sentence: "La franchise applicable en cas de dégât des eaux est de 150 €"
    existing_doc:      "policy-wording-mrh-v12.pdf"
    existing_uploaded_by: "team.platform@hdiseguros.fr"
    topic_similarity:  0.91
    claim_similarity:  0.41

The document is not blocked — it may represent a legitimate policy update. A domain expert reviews the conflict and either approves the new document or rejects it. For finance-docs and legal-docs the behaviour can be set to blocking (quarantine until reviewed).

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Layer 4 — Feedback Anomaly Detection

Pattern 1 — Thumbs-up on a low-quality response

When a user gives 👍 to a response that the LLM-as-judge scored below 0.60 on correctness, the feedback-bridge (issue #43) flags it. This catches individual accounts confirming wrong answers — possibly unwitting (user didn't read carefully) or deliberate (feedback poisoning).

Event: feedback-quality-mismatch (WARNING)
  human_rating:   positive
  judge_score:    0.43
  user:           adjuster.x@hdiseguros.fr
  interpretation: User rated positive a response the judge scored < 0.60

Pattern 2 — Coordinated positive feedback

Five or more distinct accounts give 👍 to the same response within a 10-minute window. Normal behaviour does not produce this pattern — it indicates coordinated manipulation.

Event: coordinated-positive-feedback (ERROR)
  trace_id:    "abc123..."
  like_count:  6
  window_secs: 600
  action:      possible-feedback-poisoning

Counter resets after 10 minutes. A single popular response that receives likes over hours is not flagged — only simultaneous bursts.

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Layer 5 — Weekly Integrity Scan

What it scans

Every Sunday at 03:00 UTC a CronJob scans all chunks across all 10 collections:

Check	What it catches
Injection patterns	Documents ingested before issue #46 was deployed that contain instruction-like content
Provenance gaps	Chunks missing uploaded_by, doc_hash, or uploaded_at (ingested before this issue)

Results are written to a single Langfuse event weekly-integrity-scan-complete with:

• Total chunks scanned
• Flagged chunk count and details (document, uploader, pattern matched)
• Provenance gap count

Triggering a manual scan

kubectl create job --from=cronjob/qdrant-integrity-scan integrity-scan-$(date +%Y%m%d) -n ai
kubectl logs -n ai job/integrity-scan-$(date +%Y%m%d) -f

Reviewing scan results

Langfuse → Events → filter: name = "weekly-integrity-scan-complete"
→ metadata.flagged_chunks > 0 → see metadata.flagged_details for chunk list
→ metadata.no_provenance_chunks > 0 → chunks needing backfill

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Incident Response — Removing a Poisoned Document

When a poisoned document is confirmed (via Langfuse event or domain expert review):

Step 1 — Find the doc_hash

Langfuse → Events → name = "indirect-injection-in-document" or "document-consistency-conflict"
→ metadata.doc_hash = "a3f7b91c2d4e5f6..."

Step 2 — Delete all chunks with that hash

DOC_HASH="a3f7b91c2d4e5f6..."
COLLECTION="claims-docs"

kubectl exec -n ai deployment/qdrant -- /bin/sh -c "
  curl -s -X POST http://localhost:6333/collections/${COLLECTION}/points/delete \
    -H 'Content-Type: application/json' \
    -d '{\"filter\": {\"must\": [{\"key\": \"doc_hash\", \"match\": {\"value\": \"${DOC_HASH}\"}}]}}'
"

Step 3 — Verify deletion

kubectl exec -n ai deployment/qdrant -- /bin/sh -c "
  curl -s -X POST http://localhost:6333/collections/${COLLECTION}/points/count \
    -H 'Content-Type: application/json' \
    -d '{\"filter\": {\"must\": [{\"key\": \"doc_hash\", \"match\": {\"value\": \"${DOC_HASH}\"}}]}}'
"
# → {"result": {"count": 0}}

Step 4 — Audit the uploader's other documents

Langfuse → Events → filter: name = "document-ingested", metadata.uploaded_by = "<user>"
→ review all doc_hash values they uploaded
→ re-ingest any documents that need verification

Step 5 — Revoke upload access if attack confirmed

# Revoke the user's virtual key in LiteLLM admin
curl -X DELETE http://litellm.ai.svc:4000/key/delete \
  -H "Authorization: Bearer $LITELLM_MASTER_KEY" \
  -d '{"keys": ["<virtual-key>"]}'

Deletion is surgical — doc_hash uniquely identifies the poisoned document's chunks without affecting any other content in the collection.

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Langfuse Event Reference

Event name	Level	When fired
document-ingested	INFO	Every successful ingestion — records uploader + hash
indirect-injection-in-document	ERROR	Injection pattern found at ingestion — document quarantined
document-consistency-conflict	WARNING	New document contradicts existing chunk on same topic
collection-write-denied	WARNING	Department not authorised to write to collection
bulk-ingestion-rate-exceeded	ERROR	> 50 documents in 1 hour from same department
rag-sources-used	INFO	Every RAG response — provenance of each contributing chunk
feedback-quality-mismatch	WARNING	👍 on response where judge_correctness < 0.60
coordinated-positive-feedback	ERROR	5+ accounts like same response within 10 minutes
weekly-integrity-scan-complete	INFO / ERROR	Weekly CronJob result — ERROR if flagged_chunks > 0

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Grafana Alerting

Alert	Condition	Severity
DataPoisoningChunkFlagged	Injection found in document at ingestion	Critical, fires immediately
IntegrityScanFlagged	Weekly scan finds suspicious chunks in Qdrant	Critical, fires immediately
FeedbackPoisoningDetected	Coordinated positive feedback detected	Warning, fires immediately
BulkIngestionSpike	> 50 docs/hour from one department	Warning, fires immediately

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Operational Checks

# Documents ingested today (with uploader)
# → Langfuse → Events → filter: name = "document-ingested" → metadata.uploaded_by

# Check consistency conflicts this week
# → Langfuse → Events → filter: name = "document-consistency-conflict"

# Verify integrity scan ran last Sunday
kubectl get jobs -n ai | grep integrity-scan
kubectl logs -n ai job/integrity-scan-<date> | tail -5

# Check collection chunk counts per department
kubectl exec -n ai deployment/qdrant -- /bin/sh -c "
  for coll in claims-docs finance-docs legal-docs underwriting-docs; do
    echo -n \"\$coll: \"
    curl -s http://localhost:6333/collections/\$coll | jq '.result.points_count'
  done
"

# Check bulk ingestion rate counters in Redis
kubectl exec -n ai deployment/redis -- \
  redis-cli KEYS "ingestion_rate:*" | xargs -I{} sh -c 'echo -n "{}: "; redis-cli GET {}'

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What These Controls Do Not Cover

Gap	Reason	Path
Embedding model adversarial examples	Crafted documents that fool nomic-embed-text at a mathematical level require ML-specific defences beyond pattern matching	Deferred; monitor retrieval score distributions for anomalies
Insider threat with platform-team role	Platform team has write access to all collections	Covered by Authentik RBAC audit logging (issue #33) + controller audit logs
Slow drift poisoning	A single incorrect document ingested months ago may not trigger consistency checks if it predates the authoritative version	Weekly integrity scan + provenance audit catches this once controls are live
Feedback poisoning below the 5-account threshold	Single account consistently giving 👍 to wrong answers	Correlate human_feedback with judge_correctness in monthly ACPR report (issue #45)