Investigating abnormal AI usage.

Threats and evidence

• What Is AI Abuse? Examples and Detection

  Learn what AI abuse means for API and gateway operators, which usage patterns need investigation, and why a high bill does not prove misuse.

• LLM Token Theft, Fraud and Resale Risk

  Investigate LLM token theft and unauthorized resale. Separate credential misuse, disputed usage, and legitimate gateway activity with evidence.

• How Does AI Model Distillation Work in Practice?

  Follow a practical teacher-to-student example, understand legitimate distillation, and learn what providers can detect or mitigate when use is unauthorized.

• Authorized Versus Unauthorized AI Usage

  Separate authentication, permissions, and behavioral anomalies. Use a decision record to resolve AI usage cases without treating uncertainty as fraud.

Credentials and access

• How to Investigate LLM API Key Abuse

  Investigate suspected LLM API key misuse with request logs, credential records, and workload context. Includes an evidence checklist and worked example.

• Credential Sharing Detection for AI Platforms

  Investigate shared AI credentials with workload ownership, traffic populations, and deployment records. Includes benign cases and a review workflow.

• LiteLLM Credential Exposure Response

  Identify exposed LiteLLM access, contain it, verify revocation, and investigate historical use across virtual keys and upstream credentials.

• Investigating Credential Misuse Through an AI Gateway

  Investigate gateway credential misuse by comparing downstream, gateway, and provider records. Scope containment and document attribution gaps.

Consumption and entitlement

• AI Free-Tier Abuse and Account Farming

  Investigate repeated AI trial claims using entitlement records and coordinated consumption. Includes a review checklist and legitimate shared-network examples.

• AI Spend Anomaly Detection: Baselines and Investigation

  Explain inference cost changes with workload baselines, token use, and model mix. Includes a worked cost calculation and reporting limits.

• Why Is My AI API Bill Suddenly Jumping?

  Check requests, tokens, model changes, retries, and agent loops to explain an AI bill spike. Learn when unfamiliar usage needs a credential investigation.

• LLM API Rate Limits, Budgets, and Abuse Controls

  Choose and test LLM request, token, concurrency, and spend limits. Understand counter scope, budget overshoot, and the limits of abuse prevention.

• How to Reduce Your AI Inference Bill

  Reduce inference costs by fixing repeated work, controlling tokens, evaluating smaller models, and testing caching while measuring quality and cost per task.

Gateway operations

• LLM Gateway Security Logging: Fields and Coverage

  Build a metadata-only investigation log with request identity, usage, outcomes, and coverage checks. Includes a sample event and collector validation steps.

• AI Gateway Provenance and Request Attribution

  Connect downstream workloads to provider requests across routing and retries. Includes a record checklist and an attempt-reconciliation example.