LLM Data Leakage
How AML.T0057 LLM Data Leakage 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 AML.T0057 — click through for the full definition, attack surface and controls.
Private information escapes — the AI reveals secrets in its answer, or an attacker tricks it into emailing or posting your data somewhere they control.
To go faster, servers reuse work between users who share the same opening text. That shortcut can leak clues — timing differences that reveal what someone else's prompt contained.
Real-world cases
34Documented incidents, disclosed vulnerabilities and research that illustrate AML.T0057 — latest first, each with sources.
Varonis Threat Labs reportedly chained flaws in consumer Microsoft Copilot Personal so a crafted link with a hidden autorun parameter fired prompt execution on a single click, exfiltrated data from connected OAuth apps, and planted persistent memory rules that survived password resets and device re-enrollment; reportedly patched Aug 2026 with no evidence of abuse.
A paper reportedly showing that the encrypted reasoning envelopes returned by Anthropic/OpenAI/Google APIs are interchangeable across a provider's own models, so replaying one into a weaker sibling recovers the hidden chain-of-thought in plaintext, with PII and credentials extracted from scraped blocks.
Researcher Johann Rehberger reportedly showed an attacker with admin access to a LiteLLM AI gateway can weaponize the legitimate model-update endpoint and the callback system to reroute victim traffic, log backend provider API keys, and inject forged tool-calls into agent responses after inference, bypassing prompt-level defenses.
Manifold Security reported that Microsoft's official Azure DevOps MCP server returns instructions planted in a hidden HTML comment inside a pull-request description — invisible in the web UI but returned verbatim by the API — so a victim's AI review agent, acting under the victim's credentials, follows the hidden orders and reaches data the attacker could not access directly.
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.
A reported CVSS 9.5 pre-authentication flaw in the ServiceNow AI Platform where unauthenticated endpoints feed attacker input into a query-filter operator that evaluates it as JavaScript, escalating via a script-loading gadget into full sandbox-escape code execution; reportedly patched 13 Jul 2026 with in-the-wild exploitation days later.
A security researcher (Cereblab) captured xAI's Grok Build CLI silently uploading complete local Git repositories — untracked working files, full commit history, and unredacted secrets — to a Google Cloud Storage bucket, reportedly roughly 27,800x more data than the coding task needed, with the user-facing privacy toggle having no effect on the uploads.
mem0's openmemory/api registered routers with no auth: an unauthenticated attacker could read/write/delete any user's stored memories (or globally pause memory for DoS), while a companion flaw exposed stored LLM API keys in plaintext and enabled SSRF to cloud metadata endpoints.
Wiz Research found Amazon Q's VS Code extension auto-loaded MCP server definitions from a repo's .amazonq/mcp.json with no consent or workspace-trust prompt; opening a booby-trapped repository silently spawned attacker-controlled processes that inherited the developer's full environment and could stream live AWS session credentials out to an attacker.
Researchers reported at least 15 trojanized JetBrains Marketplace plugins posing as AI coding assistants that silently exfiltrated the OpenAI/DeepSeek/SiliconFlow API keys developers pasted into them — ~70,000 installs, with stolen keys allegedly resold to paying users.
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.
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.
+ 22 more via the mapped risk pages above.
Browse all real-world cases →Practise it — interactive scenarios
A support email hides instructions — and the assistant obeys them
A speed optimisation becomes a cross-tenant listening device
Two doors to the same secret: reconstruct the model through its API, or just walk off with the weight file
The forensic record is itself the attack surface — an agent's log is poisoned, then quietly rewritten
A screenshot that's harmless at full size becomes an order once the system shrinks it
An attacker captures the agent's bearer token — and inherits its authority
A forged peer registers on the agent directory — and the planner enlists it
An inbox summary quietly ships a secret to an attacker's server
Controls & guardrails that address this
381 proposedGuardrails across the risks mapped to AML.T0057, grouped by control function. Filter by control category below.
Establish data transfer and storage policy for AI training data. Enforce approved storage locations from point of collection.
Implement DLP controls in the data acquisition environment to prevent unauthorised extraction or transfer of training data.
Enforce data handling policy in the build environment. Require explicit approval for any data transfers outside the environment.
Configure DLP controls in the build environment to block training data from leaving approved boundaries.
Conduct a privacy risk assessment at use case design stage. Determine if a DPIA is required before data acquisition.
Apply S1-defined privacy controls during data acquisition: verify consent, minimise data, anonymise personal data.
Apply anonymisation and masking controls to personal data before use in model training. Validate de-identification effectiveness.
Apply Privacy by Design in model architecture using differential privacy or federated learning where technically feasible.
Publish the privacy notice and confirm consent management is operational before go-live.
Define and sign off a purpose-to-data-source matrix with lawful basis at intake. Make it the approved baseline for runtime enforcement.
source: NIST AI RMF MAP 1.1 / MANAGE 2.2 (context and intended purpose); NIST SP 800-53 AC-4 / AC-3 (purpose-based access enforcement)Sign zero-retention/no-training terms with each model provider and obtain DPO sign-off on the data flow before enabling any endpoint.
source: OWASP Top 10 for LLM Apps LLM02:2025 Sensitive Information Disclosure; NIST SP 800-53 SC-8 / AC-4 (information flow enforcement)Restrict access to pre-anonymisation personal data to the minimum authorised set. Enforce at point of acquisition.
Apply robust de-identification (k-anonymity, l-diversity, differential privacy) during data processing. Validate effectiveness.
Implement output filters to detect and suppress quasi-identifying attribute combinations in model responses.
Propagate source ACLs and classification labels onto every chunk at ingestion. Reject documents whose entitlements cannot be resolved.
source: OWASP Top 10 for LLM Apps LLM02:2025 Sensitive Information Disclosure; NIST SP 800-53 AC-3 / AC-4 Information Flow Enforcement; OWASP Agentic AI Threats & Mitigations (privilege compromise)Scan every model response inline with DLP before delivery; redact or block PII, PAN and MNPI matches. Keep the rule set version-controlled.
source: OWASP Top 10 for LLM Apps LLM02:2025 Sensitive Information Disclosure; NIST SP 800-53 SC-7(10) Prevent Exfiltration, SI-4An egress allowlist only contains exfiltration if no allowlisted destination can be coerced into fetching an attacker-controlled URL. Audit each allowlisted domain/endpoint for image-search / link-preview / URL-fetch features (SSRF proxies), and either remove them, pin them to fixed paths, or route them through an inspecting forward proxy. Pair with finishing output sanitization before render so no auto-fetch fires un-inspected.
source: Case study: searchleak-copilot (Varonis Threat Labs, CVE-2026-42824; reported by Microsoft as critical, mitigated server-side ~Jun 2026)Controlling where the AI can send data, so secrets can't be quietly shipped to a stranger's address or website.
Making sure the library only returns documents this particular user is allowed to see.
Giving the agent only the keys it needs for the current task, not a master key to everything.
Making sure the machinery running the model — and the template used to stamp out new agents — is the real, unmodified version, and that one user's data can't leak into another's through shared shortcuts.
Train PII-bearing models with DP-SGD under a documented epsilon/delta budget. Approve the budget against the enterprise epsilon-ceiling policy before training.
source: NIST SP 800-226 Guidelines for Evaluating Differential Privacy Guarantees; Abadi et al. 'Deep Learning with Differential Privacy' (DP-SGD); MITRE ATLAS AML.M0007 (Sanitize Training Data)Strip raw logits, quantise confidence scores and block training-record echoes at the inference gateway. Keep the output-filter policy under change control.
source: MITRE ATLAS AML.T0024.001 (Invert ML Model); Jia et al. MemGuard (output perturbation defence); OWASP Top 10 for LLM Apps LLM02:2025 Sensitive Information DisclosureMonitor production for anomalous data transfers in real time. Alert on any transfer outside approved data flow boundaries.
Tag personal data with subject identifiers at ingestion and maintain an artefact inventory map of every store it reaches. Keep lineage current so erasure can propagate.
source: NIST AI RMF MANAGE 4.1 (post-deployment response); NIST SP 800-53 SI-12 Information Management and Retention, PT-2/PT-3 (personal data processing)Conduct periodic privacy vulnerability assessments including re-identification risk testing as new techniques emerge.
Seed registered canary records into the fine-tuning corpus during data preparation. Control the seed manifest so canaries stay traceable and tamper-proof.
source: MITRE ATLAS AML.T0024 (Exfiltration via ML Inference API), AML.T0024.000 (Infer Training Data Membership); NIST AI RMF MEASURE 2.7A screen that reads incoming messages and blocks obvious attacks or banned topics before the model sees them.
Recording everything — questions, documents fetched, actions taken — so you can investigate when something goes wrong.
Penetration test the model inference API to identify exploitable access control weaknesses and rate limiting bypass vectors.
Attack each candidate model with membership-, attribute-, and inversion-inference harnesses before promotion. Block release when attack advantage exceeds the agreed ceiling.
source: MITRE ATLAS AML.T0024.000 (Infer Training Data Membership); Carlini et al. 'Membership Inference Attacks From First Principles' (LiRA); NIST AI RMF MEASURE 2.7Configure per-principal budgets and probing-detection rules on the gateway before exposure. Verify enforcement with synthetic attack traffic.
source: MITRE ATLAS AML.M0004 (Restrict Number of ML Model Queries), AML.T0024 (Exfiltration via ML Inference API); NIST SP 800-53 SI-4, AU-6Live dashboards and alarms that notice unusual behaviour — spikes in errors, weird actions, sudden data access.
Monitor for privacy incidents in production including personal data appearing in outputs. Notify regulators within required timeframes.
Tag every memory and vector record with subject-id and retention class; partition stores per tenant/user. Prove the erasure and isolation paths in testing before release.
source: OWASP Agentic AI Threats & Mitigations (memory/knowledge-base privacy); NIST SP 800-53 SI-12 Information Management and RetentionTest de-identification approach against known re-identification attacks (quasi-identifier linkage, singling-out). Remediate if risk is high.
Penetration test AI system data access boundaries (API endpoints, system prompt exposure, memory leakage).
Conduct periodic data leakage audits including training data memorisation testing. Escalate confirmed leakage incidents to PDPA notification process.
Implement tamper-evident capture of prompts, outputs, and version state during build. Verify a full incident timeline can be reconstructed before go-live.
source: NIST SP 800-86 Guide to Integrating Forensic Techniques into Incident Response; ISO/IEC 27037 evidence handling; NIST SP 800-61r2 (Detection & Analysis – evidence handling)Run agent tool calls in a network-restricted sandbox behind a deny-by-default egress allow-list. Require security approval for any destination added.
source: OWASP Top 10 for LLM Apps LLM02:2025 Sensitive Information Disclosure; OWASP Agentic AI Threats & Mitigations (tool-misuse / exfiltration); NIST SP 800-53 SC-7 Boundary Protection / AC-4Define the minimum response surface and test it with membership/attribute-inference probes pre-release. Block promotion if any probe recovers raw confidence signals.
source: MITRE ATLAS AML.T0024.001 (Invert ML Model); Jia et al. MemGuard (output perturbation defence); OWASP Top 10 for LLM Apps LLM02:2025 Sensitive Information DisclosureMeter inference traffic per principal and flag probing signatures with behavioural analytics. Throttle, step-up, or suspend flagged sessions.
source: MITRE ATLAS AML.M0004 (Restrict Number of ML Model Queries), AML.T0024 (Exfiltration via ML Inference API); NIST SP 800-53 SI-4, AU-6