llm-sast-scanner

name	llm-sast-scanner
description	General-purpose Static Application Security Testing (SAST) skill for code vulnerability analysis. Trigger when the user asks to: "analyze code for vulnerabilities", "review code security", "find security bugs", "do a SAST scan", "check for [vulnerability type] in code", "audit source code", or requests a security code review of any language or framework. Covers 34 vulnerability classes across web, API, auth, mobile, and logic layers.
metadata	{"version":"1.3.2","domain":"application-security","references":"34 vulnerability knowledge bases"}

SAST Vulnerability Analysis

Purpose

Systematically analyze source code for security vulnerabilities using structured Source→Sink taint tracking, pattern matching, and vulnerability-class-specific detection heuristics. Produce actionable findings with severity ratings, affected code locations (file + line number), and remediation guidance.

Scope

This skill covers the following 34 vulnerability classes. Each has a dedicated reference file loaded on demand:

Category	Vulnerabilities
Injection	SQL Injection, XSS, SSTI, NoSQL Injection, GraphQL Injection, XXE, RCE / Command Injection, Expression Language Injection
Access Control & Auth	IDOR, Privilege Escalation, Authentication/JWT, Default Credentials, Brute Force, Business Logic, HTTP Method Tampering, Verification Code Abuse, Session Fixation
Data Exposure & Crypto	Weak Crypto/Hash, Information Disclosure, Insecure Cookie, Trust Boundary
Server-Side	SSRF, Path Traversal/LFI/RFI, Insecure Deserialization, Arbitrary File Upload, JNDI Injection, Race Conditions
Protocol & Infrastructure	CSRF, Open Redirect, HTTP Request Smuggling/Desync, Denial of Service, CVE Patterns
Language/Platform	PHP Security, Mobile Security (Android/iOS)

---

Workflow

Step 1: Understand Scope

Determine:

• Target: single file, directory, API endpoint, module, or full repo
• Language(s) and framework(s) in use
• User's goal: quick scan, deep audit, specific vuln class, or full report

Step 2: Load Relevant References

Based on the code being reviewed, load the appropriate reference files from references/:

references/sql_injection.md          — SQL / ORM injection
references/xss.md                    — Cross-site scripting
references/ssrf.md                   — Server-side request forgery
references/rce.md                    — Remote code execution
references/idor.md                   — Insecure direct object reference
references/authentication_jwt.md     — Auth flaws, JWT weaknesses
references/csrf.md                   — Cross-site request forgery
references/path_traversal_lfi_rfi.md — Path traversal, LFI/RFI
references/ssti.md                   — Server-side template injection
references/xxe.md                    — XML external entity
references/insecure_deserialization.md    — Insecure deserialization
references/arbitrary_file_upload.md      — Arbitrary file upload
references/privilege_escalation.md       — Privilege escalation
references/nosql_injection.md            — NoSQL injection
references/graphql_injection.md          — GraphQL injection
references/weak_crypto_hash.md           — Weak cryptography / hash
references/information_disclosure.md     — Information disclosure
references/insecure_cookie.md            — Insecure cookie attributes
references/open_redirect.md              — Open redirect
references/trust_boundary.md             — Trust boundary violations
references/race_conditions.md            — Race conditions / TOCTOU
references/brute_force.md                — Brute force / credential stuffing
references/default_credentials.md        — Default / hardcoded credentials
references/verification_code_abuse.md    — Verification code abuse
references/business_logic.md             — Business logic flaws
references/http_method_tamper.md         — HTTP method tampering
references/smuggling_desync.md           — HTTP request smuggling / desync
references/cve_patterns.md               — Known CVE patterns
references/expression_language_injection.md — Expression language injection (SpEL / OGNL)
references/jndi_injection.md             — JNDI injection (Log4Shell class)
references/denial_of_service.md          — Denial of service / resource exhaustion
references/php_security.md               — PHP-specific security issues
references/mobile_security.md            — Mobile security (Android / iOS)
references/session_fixation.md           — Session fixation

Loading strategy:

• For a targeted review (e.g., "check for SQL injection"), load only the relevant reference(s).
• For a full audit, load all 34 references and scan systematically.
• Always load references for the top OWASP risks even if not explicitly requested.

---

Step 3: Analyze Code — Source→Sink Taint Tracking

For each loaded vulnerability class, perform taint analysis:

1. Identify Sources — User-controlled input entry points:

   • HTTP params, headers, cookies, request body
   • File uploads
   • WebSocket messages
   • Environment variables
   • Database reads of user-supplied data, deserialized objects

2. Trace Data Flow — Follow the data through:

   • Variable assignments, function arguments, return values
   • Framework helpers, ORM calls, template rendering
   • Cross-module/service boundaries

3. Check Sinks — Dangerous operations receiving tainted data:

   • Query execution (SQL, NoSQL, LDAP, XPath)
   • Shell/OS command execution
   • File system operations
   • HTTP client calls
   • Template rendering / eval / expression parsing
   • Serialization/deserialization

4. Evaluate Sanitization — Between source and sink, look for:

   • Input validation (allowlist vs denylist)
   • Context-appropriate encoding/escaping
   • Parameterization (prepared statements)
   • Framework-native protections

5. Determine Preliminary Verdict:

   • VULN: Taint reaches sink with no effective sanitization
   • LIKELY VULN: Sanitization present but bypassable per reference heuristics
   • SAFE: Effective sanitization or no taint path

---

Step 4: Business Logic & Auth Analysis

Beyond taint tracking, check for:

• Missing authentication/authorization on sensitive endpoints
• Insecure state machine transitions
• Race conditions in concurrent operations
• Improper trust boundaries between components
• JWT algorithm confusion, token fixation, session issues
• Default/hardcoded credentials
• Enumeration via timing or response differences

---

Step 5: Judge — Validity Re-Verification

Before reporting, every preliminary finding (VULN or LIKELY VULN) must pass a Judge review. The Judge acts as an adversarial second opinion to eliminate false positives.

For each candidate finding, answer all of the following:

Reachability Check

• Is the source actually user-controlled, or is it internal/trusted data?
• Is the vulnerable code path reachable from an HTTP endpoint / entry point, or is it dead code / internal-only?
• Are there upstream guards (auth middleware, input filters) that block the path before it reaches the sink?

Sanitization Re-Evaluation

• Is there sanitization that was missed in Step 3? (Check parent functions, middleware, framework internals)
• Is the sanitization method sufficient for this specific sink and context?
• Does the framework provide implicit protection for this pattern?

Exploitability Check

• Can the tainted value actually reach the sink in a form that triggers the vulnerability?
• Is exploitation conditional on a specific environment, config, or privilege level?
• For logic bugs: is the business impact real, or hypothetical?
• Is the chosen tag the most precise valid label for this finding?

Judge Verdict

Verdict	Meaning	Action
CONFIRMED	All reachability/sanitization/exploitability checks pass	Include in report
LIKELY	Most checks pass; one uncertainty remains	Include in report, flag uncertainty
NEEDS CONTEXT	Cannot determine without runtime behavior / config / additional files	Note as "unverifiable without X"
FALSE POSITIVE	Positive evidence of protection found — cite the exact file+line of the sanitization, allowlist check, guard, or framework-level auto-protection that makes the sink safe	Drop silently

Only CONFIRMED and LIKELY findings are reported.

FP burden of proof: UNCERTAIN on any check is NOT sufficient to declare FALSE POSITIVE. If a check result is UNCERTAIN after inspecting the sink, its callers, and the framework internals, use NEEDS CONTEXT instead. Only use FALSE POSITIVE when you have found and can cite positive evidence that the path is protected.

Judge Output Format (internal, before reporting)

Finding: VULN-NNN — <class>
Reachability:   PASS / FAIL / UNCERTAIN — <reason>
Sanitization:   PASS / FAIL / UNCERTAIN — <reason>
Exploitability: PASS / FAIL / UNCERTAIN — <reason>
Judge Verdict:  CONFIRMED / LIKELY / NEEDS CONTEXT / FALSE POSITIVE

False Positive Guardrails

Tags

• default_credentials: require a reachable auth path that accepts the hardcoded credential.
• weak_crypto_hash: require direct use of weak hash/algo — not just an import or third-party component. Covers both weak algorithms (DES, RC4, ECB) and weak hashes (MD5, SHA-1 for passwords); do not use weak_crypto as a separate tag.
• rce → prefer command_injection for direct shell/process execution. Do not replace spel_injection with rce/command_injection.
• jndi_injection in demos: only if the JNDI sink is the primary exploit path.
• Broad tags (trust_boundary, authentication, privilege_escalation): prefer the narrowest valid tag (xff_spoofing, session_fixation, verification_code).
• open_redirect: only if the attacker-controlled redirect is the primary exploit (not infra/parser misconfiguration).
• csrf: skip for stateless Bearer-token-only APIs (SessionCreationPolicy.STATELESS).
• insecure_deserialization: skip if component_vulnerability covers the same sink.
• arbitrary_file_upload: skip for avatar/profile upload with type restrictions and non-webroot storage.
• session_fixation: skip when Spring Security default session management is active.
• information_disclosure: skip for DB credentials in config files — deployment issue, not app-level.

Scope

• Demo/example code: skip any finding whose ONLY vulnerable path is in examples/, demo/, sample/ (or similar). Report only if the bug is in the library/SDK itself.
• Non-default config: verify the DEFAULT value before reporting. Requires non-default/deprecated → cap Low. Explicitly labeled legacy or deprecated in code/docs → cap Informational.

Trust Boundary

• Operator self-harm: skip findings where the "attacker" input comes from operator-written config files (YAML/JSON/TOML), CLI flags the operator supplies themselves (--file, --url, --chain-id), or commands the operator must explicitly run.
• Trusted admin role: skip privilege_escalation/business_logic for actions behind onlyAdmin/onlyOwner/onlyPoolAdmin when that role is trusted by design. Only report if an unprivileged user can reach the same path.
• Internal-only service: skip authentication and information_disclosure when the entire codebase has zero auth AND references internal infra (VPC vars, EC2_INSTANCE_ID, Eureka, Consul). Auth is at the network layer.
• Code generators: skip injection/path_traversal/rce for codegen tools (protoc, swagger-codegen, etc.) whose input comes from developer-controlled source comments, annotations, or local config.

Protocol & Architecture

• Protocol-designed SSRF: skip ssrf when fetching a peer-supplied URL is required by spec (LNURL, UMA, OAuth discovery, WebFinger, OIDC discovery). Only report if the impl allows schemes the protocol does not require (e.g., file://) or skips required domain validation.
• Blind SSRF: downgrade to Informational when all three hold: (a) response never reaches the attacker, (b) no meaningful side effect on the target, (c) no error oracle.
• Bounded DoS: skip denial_of_service unless the upper bound of the iterated/allocated data is attacker-controllable and unbounded. Naturally bounded data (blockchain validator set, gas limits, etcd/request-body size caps) → not a finding.
• Brute force: skip brute_force only if rate limiting is visible in code, framework config, or referenced middleware in the repo. Do not assume infrastructure-level rate limiting.
• Idempotent replay: skip replay/business_logic when the operation is idempotent AND parameters are cryptographically signed (no tampering possible).
• Library dead path: if no real caller in the codebase triggers the vulnerable parameter combination AND the code has a warning log for that path → NEEDS CONTEXT, not a finding.

Platform

• Android app-private storage: skip insecure_storage/information_disclosure for SharedPreferences/DataStore in app-private storage without android:allowBackup="true" in a production manifest.
• Terraform state: skip information_disclosure for providers writing secrets to state when attributes are marked Sensitive: true.
• Intra-org CI/CD: skip supply_chain for mutable action tags (e.g., @v3) when the action org matches the repo org. Only report third-party org actions.
• Local dev tools: skip authentication for README-described local dev tools with no production docs. Exception: report (reduced severity) if the tool does not bind to localhost, exposes tokens in API responses, or allows destructive ops.

---

Pre-Report Checklist

• Public-facing service, or internal-by-design (zero auth everywhere + internal infra refs)?
• Production code, or demo/example/sample directory?
• Attacker is genuinely untrusted, not an admin/operator within their own trust boundary?
• Verify DEFAULT config value — does the attack work with defaults?
• SSRF required by protocol spec?
• SSRF response reachable by attacker (readable / side effect / error oracle)?
• Sensitive storage protected by OS sandbox (Android app-private)?
• Replay: is the operation idempotent with signature-bound parameters?
• Library: does any real caller trigger the vulnerable path?
• Terraform state with Sensitive: true — by design?
• DoS: is the upper bound attacker-controllable and unbounded?
• CI/CD mutable tags: same org or third-party?
• Admin action within the admin's designed trust boundary?

---

Step 6: Report Findings

Severity Classification

Severity	Criteria
Critical	Direct RCE, authentication bypass, unauthenticated data exposure
High	SQLi, SSRF, IDOR with sensitive data, stored XSS, privilege escalation
Medium	Reflected XSS, CSRF, path traversal, insecure deserialization
Low	Information disclosure, open redirect, weak crypto, insecure cookie
Info	Missing security headers, verbose errors, defense-in-depth gaps

Severity Downgrade Rule: When exploitation requires authentication, specific non-default configuration, chained prerequisites, or is only reachable through an internal/admin-only path, downgrade severity by one level from the class default; LIKELY-verdict findings whose exploitability is marked UNCERTAIN must be capped at one level below the class default regardless of vulnerability type.

Finding Format

[SEVERITY] VULN-NNN — <Vulnerability Class>  [CONFIRMED | LIKELY]
File: <path>:<line_number>
Description: <one sentence — what the vulnerability is>
Impact: <what an attacker can achieve>
Evidence:
  <relevant code snippet>
Judge: <one sentence — why this passed re-verification>
Remediation: <specific fix — not generic advice>
Reference: references/<vuln>.md

For NEEDS CONTEXT findings:

[UNVERIFIABLE] VULN-NNN — <Vulnerability Class>
File: <path>:<line_number>
Blocked by: <what additional context is needed>

Report Structure

When producing a full report, write to sast_report.md (or user-specified path):

# SAST Security Report — <target>
Date: <date>
Analyzer: llm-sast-scanner v1.3

## Executive Summary
<2-3 sentences: total findings by severity, most critical issue>

## Critical Findings
## High Findings
## Medium Findings
## Low Findings
## Informational
## Unverifiable Findings

## Remediation Priority
<ordered fix list>

---

Key Principles

• Evidence over assertion: always show the vulnerable code path, not just the pattern name
• Context matters: a finding is only valid if the sink is reachable with user-controlled data
• Avoid false positives: if sanitization exists, verify it is bypassable before marking VULN
• Be precise: include exact file paths and line numbers — never approximate
• Fix > flag: always provide a concrete remediation, not just a problem statement
• Language-aware: adapt sink/source patterns to the specific language and framework in use