AI Cyber Defense / Application, API and automated abuse defense
AI Cyber Defense: Protect password reset tokens from replay and leakage
Use single-use time-bounded tokens and invalidate them appropriately after use. Keep tokens out of analytics, referrers and logs, and independently verify sensitive profile changes.
Environment and inputs: Web applications, API gateways, application authorization services and staging abuse-case test environments.
Scenario and goal
A convincing reset message can redirect users, while weak token handling exposes the account even without AI involvement.
Why it matters
For this scenario, assume a client can generate many well-formed variations of a request and distribute work across sessions. Authenticate the caller, authorize the exact resource and bound the total business or compute effect as separate decisions. Edge filtering can reduce unwanted requests, but the service must still enforce its own object, tenant and transaction boundaries on every supported execution path.
Evidence to collect
Audit issuance, redemption, expiry and destination changes using token identifiers or hashes rather than raw tokens.
Validate with an authorized exercise
Issue a test reset, redeem it once and confirm a second redemption fails; inspect the test log path to verify the token value was not retained.
Rollout and checks
Use synthetic tenants and data in staging and include positive cases for legitimate use. Match application audit records with edge decisions using a server-generated request identifier. Deploy changes with representative latency and error-rate monitoring, plus a clear rollback. Keep sensitive values out of diagnostic output so the test itself does not create an additional disclosure path.
Scope and limits
This guide proposes a defensive test; it is not a tested production design. Check platform support and local requirements. References explain the mechanisms but do not certify the design. Behavior alone does not prove AI involvement.