Is 42Crunch good for FedRAMP moderate prep?
What middleBrick covers
- Maps findings to OWASP API Top 10, PCI-DSS, and SOC 2 themes
- Covers authentication, authorization, input validation, and LLM security
- OpenAPI 3.0/3.1/Swagger 2.0 parsing with $ref resolution
- Read-only scanning with under one minute scan time
- Continuous monitoring and diff tracking (Pro tier)
- Integrations including dashboard, CLI, GitHub Action, and MCP Server
Purpose and scope of automated scanning for FedRAMP moderate
FedRAMP moderate centers on risk management, documented controls, and continuous monitoring rather than a one time audit. The scanner supports this workflow by surfacing security findings that align with security controls described in FedRAMP moderate baselines, enabling teams to track remediation over time. It does not replace a FedRAMP assessor or authorize operation, but it can reduce manual evidence collection for selected control families.
Mapped coverage and detection capabilities
The scanner maps findings to OWASP API Top 10 (2023), PCI-DSS 4.0, and SOC 2 Type II control themes relevant to API workloads. Detection includes authentication bypass attempts, JWT misconfigurations such as alg none, sensitive data in claims, and WWW-Authenticate compliance issues. It identifies BOLA and IDOR via sequential ID enumeration and active adjacent ID probing, and detects BFLA and privilege escalation through admin endpoint probing and role/permission field leakage. Input validation checks include CORS wildcard usage with and without credentials, dangerous HTTP methods, and debug endpoints. Rate limiting and resource consumption are evaluated via rate limit header detection, oversized responses, and unpaginated arrays. Data exposure covers PII patterns such as email, Luhn validated card numbers, context aware SSN patterns, API key formats for AWS, Stripe, GitHub, and Slack, as well as error and stack trace leakage. Encryption checks include HTTPS redirect, HSTS, cookie flags, and mixed content. SSRF detection covers URL accepting parameters and body fields, internal IP detection, and active IP bypass probes. Inventory management identifies missing versioning, legacy path patterns, and server fingerprinting. Unsafe consumption surfaces related to third party URLs and webhook/callback endpoints. LLM and AI security testing performs 18 adversarial probes across Quick, Standard, and Deep tiers, including system prompt extraction, instruction override, DAN and roleplay jailbreaks, data exfiltration, cost exploitation, encoding bypasses, injection techniques, and token smuggling.
OpenAPI analysis and authenticated scanning details
The tool parses OpenAPI 3.0, 3.1, and Swagger 2.0 with recursive $ref resolution, cross referencing spec definitions against runtime findings to highlight undefined security schemes, sensitive fields, deprecated operations, and missing pagination. For authenticated scans, supported methods include Bearer, API key, Basic auth, and Cookie, gated by domain verification through DNS TXT records or an HTTP well known file to ensure only domain owners can submit credentials. Header forwarding is limited to Authorization, X-API-Key, Cookie, and X-Custom-* headers. These capabilities allow deeper coverage of authenticated control testing while maintaining a controlled scope.
Limitations and what the scanner does not do
The scanner is a detection and reporting tool and does not fix, patch, block, or remediate findings. It does not perform active SQL injection or command injection testing, as those require intrusive payloads outside its scope. Business logic vulnerabilities are not detected, since they require domain context that only a human reviewer can provide. Blind SSRF is out of scope due to the absence of out of band infrastructure detection. The tool does not replace a human pentester for high stakes audits, and it does not assess control effectiveness or compensating controls required for frameworks such as FedRAMP moderate.
Operational model, retention, and integrations
Scans complete in under a minute using read-only methods, with destructive payloads never sent. Private IPs, localhost, and cloud metadata endpoints are blocked at multiple layers. Customer data is deletable on demand and purged within 30 days of cancellation, and it is never sold or used for model training. Continuous monitoring (Pro tier) provides scheduled rescans every 6 hours, daily, weekly, or monthly, diff detection across scans, email alerts rate limited to one per hour per API, and HMAC-SHA256 signed webhooks that auto disable after five consecutive failures. Integrations include a Web Dashboard for reports and score trends, a CLI with JSON or text output, a GitHub Action for CI/CD gating, an MCP Server for AI coding assistants, and a programmatic API client for custom workflows.