Why it matters
- APIs expose custom code and business logic that static scanners cannot fully predict.
- Input validation failures often depend on parameter, role, state and sequence context.
- Fuzzing must be rate-limited, scoped and evidence-driven.
API Fuzzing
Use schema, behaviour and context to test API inputs safely and systematically.
Deterministic evidenceScope-aware executionAdaptive capability
Capability architecture
API Fuzzing: context, validation and evidence.
This capability contributes to the same platform outcome: understanding realistic attacker exposure and proving what matters.
ThreatCanary approach
- Generate tests from OpenAPI specs, observed traffic and endpoint metadata.
- Fuzz parameters, headers and bodies using safe profiles and configurable intensity.
- Create observations for anomalous responses, error behaviour, callbacks or sensitive data exposure.
What it validates or reveals
- Injection patterns, parser issues and validation gaps.
- Response anomalies and unexpected behaviour.
- Candidate findings for deeper validation.
Evidence and outputs
- A clear explanation of the exposure, affected assets and likely attack path.
- Reproducible evidence suitable for analysts, developers and risk owners.
- Prioritisation based on exploitability, business impact, sensitive data and chainability.
- Owner, remediation and workflow context that can move into Jira, Slack, SIEM or reporting.
See ThreatCanary in action
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