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An LLM gateway may be compromised when several independent signals line up: an unfamiliar identity or key, a routing or guardrail change, traffic or spending that no longer matches normal use, missing session attribution, suspicious prompts or completions, unauthorized tool calls, or downstream actions that no user requested. One strange prompt is not proof. Treat the incident as credible when identity, configuration, model, network, tool, or billing telemetry corroborates the anomaly.
This guide shows what to preserve, which records to examine, how to distinguish abuse from a noisy workload, and how to contain and recover a gateway without destroying evidence.
What a compromised LLM gateway looks like
An LLM gateway sits between users or applications and model providers. It commonly authenticates callers, selects models, applies prompt and completion controls, invokes tools, records usage, and forwards results to other systems. An attacker who gains control of the gateway can steal keys, impersonate tenants, bypass safety policies, redirect requests to an unknown model, run up token charges, or turn model output into an unauthorized action.
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Compromise is therefore a correlation problem rather than a single-indicator problem. Establish the normal identity, request, model, latency, token, tool, network, and cost profile for each tenant and workload. Then look for changes that agree across more than one data source. NIST API guidance (SP 800-228) treats API lifecycle risk analysis and runtime controls as essential, while NIST token guidance emphasizes verification, key management, lifecycle controls, and continuous monitoring. OWASP guidance extends those controls to prompt injection, output handling, excessive agency, runtime isolation, and auditability.
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Signals that deserve immediate investigation
- A new principal, API key, token, route, model, plugin, deployment, or elevated role appears without an approved change.
- A known credential is used from an unfamiliar location, tenant boundary, application, or user agent, or authorization failures rise sharply.
- Verification, rate limits, guardrails, logging sinks, or policy settings are disabled or weakened.
- Request volume, token counts, response sizes, model mix, latency, errors, or spend depart abruptly from the established baseline.
- Prompts or completions repeatedly ask for secrets, policy bypasses, hidden instructions, or access to tools beyond the caller’s authorization.
- Tool calls, external requests, file access, account changes, or other downstream actions have no matching user intent.
- Session, human-principal, tenant, or agent identifiers disappear from otherwise valid requests.
- Audit records have unexplained gaps, are routed to a new sink, or contain credentials and sensitive content that should not be logged.
Preserve evidence before changing the gateway
Containment is important, but deleting or overwriting records can make it impossible to determine what happened. Start an incident timeline in a trusted system and record who approved every emergency action. Use synchronized UTC timestamps and preserve the original copies as read-only or write-once data where possible.
- Export gateway access logs. Include request IDs, timestamps, tenant and application identifiers, authentication outcome, route, model, status code, latency, token counts, and response size. Redact or tokenize sensitive prompt and response content rather than copying it into a broadly accessible location.
- Export identity-provider events. Capture sign-ins, multifactor changes, role or group changes, service-principal creation, consent events, key issuance, key use, and revocation. Preserve source IP, device or workload identity, and the exact time.
- Preserve key and token records. Save issuance, rotation, verification, failure, refresh, and revocation events for API keys, access tokens, refresh tokens, signing keys, and service credentials. NIST identifies forgery, theft, misuse, and weak lifecycle controls as material token risks.
- Capture model-provider and routing logs. Record which provider and model handled each request, the configured fallback chain, deployment identifiers, and any changes to model aliases, safety settings, or routing rules.
- Capture tool and plugin activity. Preserve tool name, arguments after appropriate redaction, calling principal, approval decision, result, destination, and execution status. Include plugin installation, update, and permission changes.
- Export network-flow and infrastructure telemetry. Keep DNS, firewall, proxy, workload, container, and egress records that can show unusual destinations, data transfer, or lateral movement.
- Preserve billing and usage data. Export token consumption, request counts, model mix, quotas, invoices, and spend alerts. Keep the provider’s original timestamps and account or tenant scope.
- Verify evidence integrity. Hash exported files, record the export operator and time, and restrict access. Missing context, disabled audit sinks, or unexplained gaps are themselves potential indicators.
Check identity, credentials, and configuration
Start with the control plane because an attacker can make later traffic appear legitimate by changing identities or policy. Compare current state with the last approved configuration and the normal inventory of principals, keys, routes, models, plugins, and deployments.
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Identity and token checks
- List every human, service, agent, workload, and tenant principal that authenticated during the suspected period. Investigate unknown principals, newly created accounts, privilege escalation, disabled multifactor authentication, and tokens used outside the expected application or geography.
- Compare key and token use with issuance records. A token used before it was issued, after revocation, by an unexpected workload, or concurrently from distant locations suggests theft, replay, clock abuse, or a verification failure.
- Look for hardcoded or long-lived secrets in gateway configuration, source repositories, images, and deployment manifests. Replace exposed values rather than merely hiding them in logs.
- Check whether signature, audience, issuer, expiry, scope, or tenant checks were relaxed. A valid-looking token is not sufficient if verification or authorization rules were changed.
Configuration and deployment checks
- Review configuration history for changed routes, model aliases, fallback providers, prompt filters, completion filters, rate limits, logging destinations, network allowlists, and tool permissions.
- Find orphaned deployments, abandoned endpoints, test routes exposed to production, and models or plugins that are not in the approved inventory. OWASP identifies unsecured APIs and orphaned deployments as recurring risks.
- Compare running images, packages, policy files, and infrastructure definitions with a known-good version. Record the change author, deployment mechanism, approval, and rollback target.
- Check whether an attacker redirected logs, disabled an audit sink, or reduced retention. A clean-looking dashboard can be evidence of tampering if the underlying stream is incomplete.
Analyze traffic, model use, and cost
Build a baseline separately for each tenant, application, route, model, and time period. A global average hides abuse in a small tenant and can make a legitimate batch job look malicious. Compare at least request rate, token counts, prompt and completion sizes, latency, error rate, model selection, source geography, headers, and spend.
| Telemetry | Possible compromise signal | Useful corroboration |
|---|---|---|
| Request rate and repetition | Sudden spikes, scraping-like repetition, or sustained traffic outside operating hours | Same token or principal across many tenants, repeated prompts, or a new source network |
| Tokens and response size | Unexpectedly large prompts or completions, or a sharp change in input-to-output ratio | Prompt attempts to extract context, hidden instructions, or secrets; unusual model selection |
| Latency and errors | New latency distribution, repeated authorization failures, or a surge in provider errors | Retries from one credential, altered routing, or rate-limit settings changed shortly beforehand |
| Model mix | Requests to an unapproved model, region, provider, or fallback route | Routing configuration change, unknown deployment, or provider-account activity |
| Headers and geography | Unusual headers, source locations, or traffic crossing tenant boundaries | Identity events, VPN or proxy logs, and network-flow records that identify the workload |
| Spend and quotas | Token cost or quota consumption that cannot be explained by an approved job | Billing records tied to a new key, model, tenant, or time window |
Do not label a spike malicious solely because it is large. Scheduled evaluations, incident testing, a newly launched feature, or a provider outage can produce the same shape. Require a second signal such as an unapproved identity, configuration change, or downstream action before declaring compromise.
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Inspect prompts, completions, and tool calls safely
Prompts and model outputs are untrusted data, including content produced by an internal user or retrieved from a trusted-looking document. OWASP’s LLM Verification Standard says that all prompts should be treated as untrusted and subjected to deployed security controls, and that guards should scan prompts and completions for potential prompt injection.
Prompt and completion indicators
- Repeated instructions to ignore system or developer rules, reveal hidden prompts, disclose credentials, or disable safety controls.
- Indirect injection carried through documents, web pages, tickets, code comments, images, or other retrieved content.
- Outputs containing secrets, authorization headers, internal URLs, personal data, or policy text that the requesting principal should not receive.
- Sudden changes in output entropy, language, format, or refusal behavior that coincide with a policy or model change.
- Model output being passed directly into shell commands, database queries, messages, account changes, or code execution without validation and authorization.
Tool and downstream-action indicators
- A tool is called by a route, model, tenant, or principal that has never been permitted to use it.
- Arguments exceed the user’s scope, target a new host, access another tenant’s data, or appear designed to exfiltrate content.
- Calls occur without a corresponding user request, approval record, or session identifier.
- A model’s response causes a high-impact action even though the gateway did not verify authorization at the point of execution.
- Plugins or extensions make network requests, read files, or persist data outside their documented runtime boundary.
Correlate each suspicious prompt or completion with identity, route, tool, and downstream logs. Do not place full sensitive prompts, responses, credentials, or authorization headers in a general-purpose log index; use redaction, structured excerpts, access controls, and a protected evidence store.
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Use log integrity as a detection signal
Request and response logging is the foundation of abuse detection and incident investigation, according to OWASP AISVS. For every important event, you should be able to connect the gateway request to a human or workload principal, tenant, agent session, model invocation, tool call, and resulting external action.
- Search for requests with missing or contradictory session, tenant, human-principal, or agent identifiers.
- Compare gateway records with identity-provider, model-provider, tool, network, and billing streams. A request present in one system but absent from the others may indicate a logging failure or evasion.
- Check retention, clock synchronization, sequence numbers, and ingestion delays. A time gap that begins immediately after a configuration change is more suspicious than a short, documented collector outage.
- Look for secrets or complete sensitive payloads in logs. This can indicate unsafe instrumentation and creates a second incident even if the gateway itself was not breached.
- Verify that administrators cannot silently delete or rewrite evidence. Separate administration of the gateway from administration of the audit store, and alert when sinks, policies, or retention are changed.
Separate compromise from false positives
Use a hypothesis table for each anomaly: what changed, which principal could cause it, which systems confirm it, and what benign explanation remains. A single abnormal prompt can be a malicious test, a copied example, a red-team exercise, or an accidental user request. Likewise, a cost spike may be a legitimate batch process.
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Raise confidence when independent evidence agrees: an unfamiliar key is created, that key appears in a new geography, its requests use an unapproved model, and those requests trigger tool calls or unusual spend. Lower confidence when the event matches an approved change ticket, scheduled workload, known provider incident, or documented test, and all expected audit records are present.
Contain a suspected compromise
Containment should stop further abuse while preserving enough access for investigation. Use an incident commander and document each action and its time.
- Revoke and rotate credentials. Revoke exposed API keys, access and refresh tokens, signing keys, and service credentials. Create replacements with least privilege and short lifetimes; do not reuse a compromised secret.
- Disable suspicious access. Suspend unknown or escalated principals, affected sessions, routes, models, plugins, and tools. Preserve their configuration and logs before deletion.
- Apply emergency limits. Reduce rate and spend quotas, block untrusted source networks, and require stronger authentication or manual approval for high-impact operations.
- Isolate workloads and tenants. Separate affected tenants, queues, runtimes, and network segments. Stop cross-tenant data paths and outbound destinations that are not essential.
- Quarantine untrusted content. Move suspicious prompts, retrieved documents, and outputs to a zero-tool path where they cannot trigger external actions. Treat model output as untrusted input.
- Protect downstream systems. Review database, storage, messaging, identity, finance, and other connected systems for unauthorized reads, writes, account changes, or data transfer. Revoke downstream tokens if they could have been exposed.
- Preserve forensic copies. Snapshot the gateway, deployment manifests, policy files, container images, and relevant logs before cleanup or rollback.
Recover and prove the gateway is clean
- Determine the entry point and scope. Identify the first unauthorized identity or configuration event, affected tenants and models, exposed data, tool actions, and the earliest reliable timestamp.
- Restore a known-good configuration. Use a reviewed, versioned gateway configuration and controlled rollback. Rebuild compromised images or runtimes rather than assuming that a changed host is trustworthy.
- Re-establish authorization boundaries. Reissue credentials, verify token claims and key rotation, confirm tenant isolation, and require explicit authorization at every tool or external-action boundary.
- Test security controls. Confirm prompt and completion guards, rate limits, spend alerts, logging, model allowlists, network egress rules, and rollback procedures with safe test cases.
- Reconcile records. Compare gateway, identity, provider, tool, network, and billing data to account for every request and action during the incident window.
- Monitor in heightened mode. Keep tighter thresholds, detailed but redacted telemetry, and frequent review until traffic, identity use, configuration, and cost remain consistent with approved behavior.
- Notify affected parties as required. Follow contractual, regulatory, provider, and organizational incident-reporting obligations based on the data and systems involved.
Controls that make future detection faster
When selecting or improving an LLM gateway, evaluate the controls as one system rather than buying a prompt filter in isolation.
| Control area | Questions to verify |
|---|---|
| Identity and authorization | Can every request be tied to a human, service, agent, tenant, and session? Are permissions checked again before a tool or external action? |
| Key and token lifecycle | Are issuance, scope, expiry, rotation, revocation, signature, audience, and issuer checked and monitored? |
| Tenant and runtime isolation | Can prompts, memory, files, plugins, queues, and network destinations cross tenant or workload boundaries? |
| Request, response, and tool telemetry | Are model, route, latency, tokens, status, tool arguments, approvals, results, and downstream actions correlated by a stable identifier? |
| Prompt and completion guards | Are all prompts and outputs treated as untrusted, scanned for injection and sensitive data, and blocked or quarantined when policy requires? |
| Rate and spend controls | Can limits be set per principal, tenant, route, model, and time window, with alerts for unusual usage? |
| Configuration integrity | Are changes versioned, approved, monitored, tamper-evident, and reversible to a known-good state? |
| Downstream authorization | Does the receiving system enforce its own authorization instead of trusting model output or gateway labels? |
| SIEM and incident response | Can protected logs be exported quickly, correlated across systems, retained adequately, and used by responders without exposing secrets? |
What evidence can actually prove a compromise?
No single log line proves that an LLM gateway was hacked. The strongest case is a chain: an unauthorized identity or changed control, authenticated use by that identity or credential, behavior outside the baseline, and a resulting tool, data, or billing effect. Preserve both positive evidence and the absence of expected controls, such as disabled verification or missing audit records.
There is no authoritative frequency statistic that establishes how often LLM gateways are compromised. The defensible approach is indicator-based detection, continuous monitoring, and an incident process that can reconstruct requests and actions without exposing the sensitive content being investigated.
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