Use when reviewing code for security vulnerabilities, implementing authentication/authorization, handling user input, or discussing web application security. Covers OWASP Top 10:2025, ASVS 5.0, LLM Top 10 (2025), and Agentic AI security (2026).

OWASP Security Apply these standards when writing or reviewing code. For a review, follow the workflow below. Reference files (read the one the task needs, and only the section you need): [ reference/review checklist.md ](reference/review checklist.md): coverage checklist for every Top 10 category, plus LLM and agent checks. Read during step 3 of a review. [ reference/languages.md ](reference/languages.md): per language pitfalls with unsafe/safe examples for 20+ languages. Read the section for the language under review. [ reference/config and supply chain.md ](reference/config and supply chain.md): A02 and A03 in Dockerfiles, Kubernetes, Terraform, framework config, security headers, lockfiles, and CI/CD. Read when the change touches config, IaC, dependencies, or pipelines. [ reference/owasp report.md ](reference/owasp report.md): attack vectors, mitigations, and worked examples for every Top 10:2025, ASVS 5.0, LLM Top 10, and Agentic item. About 1100 lines: jump to the section you need. Security Review Workflow Copy this checklist into your response and tick it off as you go: Step 1: Map entry points and trust boundaries. List where attacker controlled data enters: routes and handlers, headers and cookies, uploads, webhooks, queue consumers, CLI arguments, third party API responses, and anything an LLM reads or returns. Note where authentication and authorization are enforced; it is often centralized in middleware rather than per route. Step 2: Load the references this code needs. The language section of languages.md ; config and supply chain.md if config, IaC, dependencies, or CI changed; the LLM and Agentic sections of owasp report.md if the code calls a model or runs an agent. Step 3: Sweep for candidate issues. Walk review checklist.md for the categories the code touches. For each candidate, trace the path from an entry point in step 1 to the sink. Step 4: Triage every candidate with the rubric in "Before Reporting a Finding" below. Drop candidates that fail it, or downgrade them to defense in depth. If a candidate's reachability is unclear, go back to step 1 for that input before deciding. Step 5: Report findings in the format below, highest severity first. Before Reporting a Finding A pattern match is not a vulnerability. The most common failure mode in automated security review is reporting unreachable or already mitigated code, which buries the real findings. Confirm all four before reporting: 1. Is the input actually attacker controlled? Trace it back to a real entry point: a request parameter, header, cookie, uploaded file, webhook, queue message, or third party API response. A value that only ever comes from a constant, an enum, or trusted internal config is not an injection source. 2. Is the sink reachable with that input? Check whether validation, an allowlist, an ORM, or a framework level control already sits between them. Look for auth middleware ( middleware.ts , proxy.ts , Express/Django/Rails middleware, a base controller, decorators) before flagging a route as missing authorization. Enforcement is often centralized rather than per route. 3. What is the blast radius? Who can trigger it, what do they get, and does it cross a trust boundary? An SSRF reaching cloud metadata differs from one reaching localhost only. 4. Can the attacker perform every step? Each step of the exploit must be possible from the attacker's position. A symlink race needs a way to create symlinks on the server; a header attack needs a client that can set that header. If a step needs a capability the code doesn't show the attacker having, the finding is "Needs verification", not High. Report severity by exploitability, not by pattern. State the concrete path ( this input reaches this sink ) and say so explicitly when a finding is theoretical or defense in depth rather than directly exploitable. If reachability can't be determined from the code available, say that instead of asserting either way. Reporting Format One block per finding, highest severity first: Write every LLM and ASI ID with its risk name, e.g. "LLM03 Excessive Agency". A bare LLM ID is ambiguous: the 2025 and 2026 editions use the same numbers for different risks. Severity Meaning Critical Unauthenticated remote code execution, auth bypass, or mass data exposure High Authenticated exploitation crossing a trust boundary (IDOR into other tenants, SQLi behind login) Medium Needs unusual preconditions, or impact is limited to the attacker's own data Low Defense in depth gap with no demonstrated exploit path Info Hardening suggestion; say plainly that it is not a vulnerability If the review finds nothing exploitable, say so directly. Do not pad the report with Info items to look thorough; a long list is what makes real findings get ignored. OWASP Top 10:2025 Three categories were renamed from 2021 and two are new (A03, A10). Use these names and numbers; much OWASP material online still cites the 2021 list. Category Key Prevention A01 Broken Access Control (now includes SSRF) Deny by default, enforce server side, verify ownership A02 Security Misconfiguration Harden configs, disable defaults, minimize features A03 Software Supply Chain Failures Lock versions, verify integrity, audit dependencies A04 Cryptographic Failures TLS 1.2+, AES 256 GCM, Argon2/bcrypt for passwords A05 Injection Parameterized queries, input validation, safe APIs A06 Insecure Design Threat model, rate limit, design security controls A07 Authentication Failures MFA, check breached passwords, secure sessions A08 Software or Data Integrity Failures Sign packages, SRI for CDN, safe serialization A09 Security Logging and Alerting Failures Log security events, structured format, alerting A10 Mishandling of Exceptional Conditions Fail closed, hide internals, log with context OWASP Top 10 for LLM Applications (2026) For applications that call LLMs (chatbots, RAG, copilots, agents). The 2026 edition renumbered the list; translate 2025 IDs with the table in owasp report.md and cite 2026 IDs only. Risk Key Mitigation LLM01 Prompt Injection No complete fix exists. Fence untrusted content (including images, audio, tool output), keep privileges out of the model's reach, filter outputs LLM02 Sensitive Information Disclosure Sanitize training/RAG data, strip PII from context, restrict what the model can retrieve per user LLM03 Excessive Agency Minimize tools and permissions, require human approval for destructive actions, scope credentials per task LLM04 Supply Chain Verify model provenance and signatures, vet third party model hubs, lock model + adapter versions LLM05 Data and Model Poisoning Validate training/fine tuning sources, anomaly detect on data ingestion, hold out integrity tests LLM06 Unbounded Consumption Rate limit per user/key, cap tokens and tool calls per request, monitor cost, set hard timeouts LLM07 Misinformation Cite sources, surface confidence, require grounding for high stakes answers, disclose AI provenance LLM08 Hidden Context Exposure Assume the system prompt, tool schemas, and other hidden context are extractable: no secrets there, and no authorization or policy that relies on them staying hidden LLM09 Vector and Embedding Weaknesses Tenant isolate vector stores, access control on retrieval, sign or hash chunks against indirect prompt injection LLM10 Improper Output Handling Treat all LLM output, including generated code, as untrusted input: validate, escape, or sandbox before any sink (SQL, shell, HTML, code, tool calls) OWASP Top 10 for Agentic Applications (2026) For AI agent systems that plan, call tools, or keep memory: Risk Description Mitigation ASI01: Agent Goal Hijack Prompt injection alters agent objectives Treat tool and retrieved content as data, goal boundaries, behavioral monitoring ASI02: Tool Misuse & Exploitation Tools used in unintended ways Least privilege, fine grained permissions, validate I/O ASI03: Identity & Privilege Abuse Delegated trust, inherited credentials, role chain exploits Short lived scoped tokens, identity verification ASI04: Agentic Supply Chain Vulnerabilities Compromised plugins/MCP servers Verify signatures, sandbox, allowlist plugins ASI05: Unexpected Code Execution Unsafe code generation/execution Sandbox execution, static analysis, human approval ASI06: Memory & Context Poisoning Corrupted RAG/context data Validate stored content, segment by trust level ASI07: Insecure Inter Agent Communication Spoofing/intercepting agent to agent messages Authenticate, encrypt, verify message integrity ASI08: Cascading Failures Errors propagate across systems Circuit breakers, graceful degradation, isolation ASI09: Human Agent Trust Exploitation Over trust in agents leveraged to manipulate users Label AI content, user education, verification steps ASI10: Rogue Agents Compromised agents acting maliciously Behavior monitoring, kill switches, anomaly detection ASVS 5.0 Key Requirements ASVS 5.0 (May 2025) renumbered and reorganized every chapter. 4.0 requirement IDs do not map to 5.0 — V2.1.1 meant "password length" in 4.0 and means something else now. Cite 5.0 IDs only. Levels are defined by share of requirements, not by application category: Level Share Intent L1 ~20% Minimum bar; deliberately small to lower the barrier to entry L2 ~50% (≈70% cumulative) What most applications should target L3 remaining ~30% Highest assurance Level 1 — the minimum bar Passwords at least 8 characters ; 15+ strongly recommended (6.2.1) No composition rules — permit any characters, paste, and password managers (6.2.5, 6.2.7) Block at least the top 3000 common passwords (6.2.4) Anti automation against credential stuffing and brute force (6.3.1) No default accounts like root / admin / sa (6.3.2) Reference session tokens from a CSPRNG with 128+ bits entropy (7.2.3) New session token issued on authentication and re authentication (7.2.4) Session fully unusable after logout or expiry (7.4.1) Function level and data level access restricted to explicit permissions (8.2.1, 8.2.2) Authorization enforced at a trusted service layer the client cannot manipulate (8.3.1) Parameterized queries / ORM for all data access (1.2.4); parameterized OS calls (1.2.5) Context appropriate output encoding for HTML, URLs, and JavaScript/JSON (1.2.1–1.2.3) Avoid eval() and dynamic code execution (1.3.2) Input validated at a trusted service layer, positive/allowlist where possible (2.2.1, 2.2.2) TLS 1.2+ on all external traffic, publicly trusted certificates (12.1.1, 12.2.1, 12.2.2) Approved ciphers and modes only — no ECB, no PKCS 1 v1.5 padding (11.3.1, 11.3.2) No sensitive data in URLs or query strings (14.2.1) Level 2 — what most applications should target MFA, or a documented combination of single factors (6.3.3) Passwords checked against a breached password set (6.2.12) No forced periodic password rotation — rotate only on compromise (6.2.10) All security logging starts here. ASVS 5.0 has no L1 logging requirements; the whole of V16 is L2+. Log authentication attempts, failed authorization, security events, and unexpected errors (16.3.1–16.3.4) Log entries carry when/where/who/what metadata on a synchronized clock (16.2.1, 16.2.2) Logs encoded against log injection, protected from modification, shipped off box (16.4.1–16.4.3) Generic error message to the user; detail stays in the log (16.5.1) Level 3 — highest assurance ASVS 5.0 has 92 L3 requirements ; they are not enumerated here. Two worth knowing because they tighten

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