Bettinamindless552 Appsec Skills Eresus Sast Scanner

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 vulnerability knowledge file loaded on demand:

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 Knowledge Files

Based on the code being reviewed, load the appropriate vulnerability knowledge 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 knowledge file(s).
• For a full audit, load all 34 coverage files and scan systematically.
• Always load knowledge files 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

Only CONFIRMED and LIKELY findings are reported.

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

• Do not emit default_credentials unless there is a reachable authentication path that accepts a hardcoded, documented, factory, or seeded credential pair.
• Do not emit weak_crypto_hash when the evidence only shows a vulnerable third-party component or a generic cryptography import. Require direct use of weak hashes, broken password storage, or unsafe signing.
• Tag clarification: weak_crypto_hash is the canonical tag for both weak cryptographic algorithms (DES, RC4, ECB mode) and weak hash functions (MD5, SHA-1 for passwords). Do not use weak_crypto as a separate tag unless the benchmark ground truth explicitly requires it; prefer weak_crypto_hash to avoid tag fragmentation.
• Do not emit generic rce when the code shows direct shell/process execution — prefer command_injection.
• Do not replace spel_injection with command_injection or rce when the exploit primitive is Spring EL expression parsing.
• Do not emit jndi_injection for component demos unless the JNDI sink itself is the primary exploit path.
• Do not emit broad tags (trust_boundary, authentication, privilege_escalation) when a narrower precise tag is supported (xff_spoofing, session_fixation, verification_code).
• Do not emit open_redirect for infrastructure/parser misconfiguration unless attacker-controlled redirect response is the primary exploit.
• Do not emit denial_of_service merely because a path lacks size/rate limits — require explicit evidence that resource exhaustion is the primary impact.
• Do not emit brute_force merely because a login endpoint lacks visible rate limiting — rate limiting may exist at infrastructure level. Require explicit evidence of unlimited attempt processing.
• Do not emit csrf for stateless APIs using Bearer-token-only authentication with SessionCreationPolicy.STATELESS.
• Do not emit insecure_deserialization when the same sink is already covered by component_vulnerability (e.g., Fastjson autoType) unless a separate deserialization path exists.
• Do not emit arbitrary_file_upload for profile/avatar image upload with type restrictions and non-webroot storage.
• Do not emit session_fixation when Spring Security default session management is active (migrateSession is the default).
• Do not emit information_disclosure for database credentials in application config files — these are deployment issues, not application-level disclosure.

Step 6: Report Findings

Severity Classification

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>

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: eresus-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

Source: Bettinamindless552/appsec-skills — distributed by TomeVault.