Payment APIs security

Payment APIs move money and data, so the threat model centers on authentication bypass, data exposure, and unauthorized operations. Black-box scanning probes public endpoints without code or cloud access, focusing on behaviors visible over the wire. This approach surfaces risky configurations, injection surfaces, and data leakage that an external attacker can observe.

Critical risks include weak authentication controls, excessive data exposure in responses, insecure direct object references, and unsafe integrations that extend the attack surface. The scanner exercises read-only methods and text-only POST probes, avoiding destructive payloads while still revealing exploitable patterns.

Compliance mapping for payment APIs focuses on PCI-DSS 4.0 and OWASP API Top 10 (2023). Findings are mapped to these frameworks to highlight gaps in access control, encryption, and input validation that affect auditability.

The scanner checks authentication mechanisms including Bearer tokens, API keys, Basic auth, and cookies. It tests for JWT misconfigurations such as alg=none, weak signing keys, expired tokens, missing claims, and exposure of sensitive data in claims.

Security headers and WWW-Authenticate compliance are evaluated to ensure proper challenge and protection against downgrade attacks. BOLA and IDOR are tested via sequential ID enumeration and active adjacent-ID probing to detect insecure direct object references.

Authenticated scanning requires domain verification through DNS TXT records or HTTP well-known files, ensuring only the domain owner can submit credentials. Header allowlist is limited to Authorization, X-API-Key, Cookie, and X-Custom-* to reduce unintended side effects.

Data exposure checks identify PII patterns such as email addresses, Luhn-validated card numbers, context-aware Social Security Numbers, and API key formats for AWS, Stripe, GitHub, and Slack. Error and stack-trace leakage is also detected.

Input validation testing covers CORS wildcard configurations with and without credentials, dangerous HTTP methods, and debug endpoints. These findings highlight improper access control and information leakage that can aid external attackers.

OpenAPI analysis parses OpenAPI 3.0, 3.1, and Swagger 2.0 with recursive $ref resolution. The spec is cross-referenced against runtime results to find undefined security schemes, sensitive fields, deprecated operations, and missing pagination that increase risk.

The scanner includes LLM / AI Security testing with 18 adversarial probes across Quick, Standard, and Deep tiers. These probes target system prompt extraction, instruction override, DAN and roleplay jailbreaks, data exfiltration, cost exploitation, and token smuggling.
Additional techniques include base64/ROT13 encoding bypass, translation-embedded injection, few-shot poisoning, markdown injection, multi-turn manipulation, indirect prompt injection, and nested instruction injection.

These tests are read-only and designed to surface model manipulation risks without executing destructive actions. The goal is to highlight configuration and design weaknesses in AI-facing endpoints rather than to provide remediation for model behavior.

Pro tier features include scheduled rescans every 6 hours, daily, weekly, or monthly, with diff detection across scans to track new findings, resolved findings, and score drift. Email alerts are rate-limited to one per hour per API, and HMAC-SHA256 signed webhooks auto-disable after five consecutive failures.

The scanner does not fix, patch, block, or remediate findings; it detects and reports with guidance. It does not perform active SQL injection or command injection testing, does not detect business logic vulnerabilities, and does not replace a human pentester for high-stakes audits.

Customer data is deletable on demand and purged within 30 days of cancellation. It is never sold and is not used for model training. The tool supports integration via Web Dashboard, CLI, GitHub Action, MCP Server, and a programmable API client.