Security Review

Language-agnostic security review based on OWASP Secure by Design.

Checklist (OWASP Top 10:2025)

Updated to OWASP Top 10:2025 (released January 2025). Previous 2021 edition had different groupings.

A01:2025 — Broken Access Control

• Least privilege enforced (users get minimum permissions needed)
• Authorization checked on EVERY request (not just the first)
• CORS restrictive (not

  *

  )
• Directory listing disabled
• Rate limiting on API/controller access

A02:2025 — Cryptographic Failures

• Data encrypted at rest and in transit (TLS 1.2+)
• No secrets in code, logs, or error messages
• PII identified and classified in threat model
• Passwords hashed with bcrypt/argon2 (never MD5/SHA1)
• Cryptographic algorithms current (no deprecated ciphers)

A03:2025 — Injection

• All user input validated (type, length, range, format)
• Parameterized queries for ALL data access (never string concatenation)
• Input allowlisting preferred over denylisting
• Output encoded based on context (HTML, JS, URL, CSS — covers XSS)
• Content Security Policy configured

A03b:2025 — Software Supply Chain Failures (new in 2025)

• SBOM (Software Bill of Materials) maintained for critical dependencies
• Build integrity verified (reproducible builds, signed artifacts)
• Dependency provenance checked (not just version, but source authenticity)
• Transitive dependencies audited (not just direct)
• Lock files committed and verified

A04:2025 — Insecure Design

• Threat modeling performed (STRIDE — see /threat-model)
• Secure design patterns used (defense in depth, fail secure)
• Business logic abuse cases considered
• Security requirements defined alongside functional requirements

A05:2025 — Security Misconfiguration

• Default credentials changed
• Unnecessary features/ports disabled
• Security headers set (CSP, HSTS, X-Frame-Options, X-Content-Type-Options)
• Error handling does not expose stack traces

A06:2025 — Vulnerable and Outdated Components

• Dependency audit run (no known critical vulnerabilities)
• Dependencies pinned to specific versions
• Automated scanning in CI
• Unused dependencies removed

A07:2025 — Identification and Authentication Failures

• Session IDs regenerated on login
• Multi-factor authentication available for sensitive operations
• Credential stuffing protections (rate limiting, account lockout)
• Password strength requirements enforced

A08:2025 — Software and Data Integrity Failures

• CI/CD pipeline integrity verified (no unsigned code execution)
• Deserialization inputs validated
• Software supply chain reviewed (SBOMs for critical dependencies)
• Auto-update mechanisms use signed packages

A09:2025 — Security Logging and Monitoring Failures

• Security events logged (login attempts, auth failures, access denials)
• No sensitive data in logs
• Alerting on anomalous patterns
• Logs tamper-resistant (append-only or forwarded to SIEM)

A10:2025 — Server-Side Request Forgery (SSRF)

• URL inputs validated and allowlisted
• Internal network access restricted from user-supplied URLs
• Response content not returned directly to users without sanitization

A10b:2025 — Mishandling of Exceptional Conditions (new in 2025)

• All error paths explicitly handled (no silent failures)
• Resource exhaustion scenarios addressed (memory, disk, connections)
• Timeout and retry policies defined for all external calls
• System fails closed (denies access on error, not grants)

OWASP Top 10 for LLM Applications (2025)

Apply for

ai_tool

product types. Source: owasp.org/www-project-top-10-for-large-language-model-applications

• Prompt Injection: User input cannot override system instructions (direct or indirect)
• Data Poisoning: Training/fine-tuning data sources validated and auditable
• Insecure Output Handling: LLM output sanitized before use in downstream systems (SQL, shell, HTML)
• Model Denial of Service: Rate limiting and resource caps on inference requests
• Supply Chain: Model provenance verified; third-party models/plugins audited
• Sensitive Information Disclosure: PII/secrets not leaked in model responses; training data scrubbed
• Insecure Plugin/Tool Design: Tool permissions follow least privilege; tool outputs validated
• Excessive Agency: Model actions bounded; human-in-the-loop for destructive operations
• Overreliance: Users informed of model limitations; confidence indicators provided
• Model Theft: Model weights and fine-tuning data access-controlled

Stack-Specific Tools

Consult

.claude/jit-tooling/security-scanning.md

for tool selection per stack.