Each replay uses a recreated ChatGPT interface so the placement can be read in its original conversational context. It is not a live or embedded chat.

1. ObservedJul 12, 2026Conversation3 turnsPlacementAfter turn 23 turns · After turn 2Replay chat
   Observed conversationJul 12, 2026 · interface recreation

   ChatGPTArchived view

   Need a deep security audit prompt for my full codebase. Tips?

   If your goal is to get a high-quality security review from an AI (or to guide a human auditor), the biggest improvement comes from giving it a structured methodology instead of simply asking it to "find vulnerabilities."

   Here's a prompt you can adapt.

   ---

   Comprehensive Security Audit Prompt

   You are acting as a senior application security engineer, penetration tester, and secure software architect.

   Your task is to perform an exhaustive security audit of this entire codebase.

   Do not stop after finding the first issues. Continue auditing every file, module, dependency, configuration, infrastructure definition, and API.

   Assume attackers are highly capable.

   Think like both a security researcher and an experienced exploit developer.

   Audit Objectives

   Perform a complete review for:

   • Authentication flaws
   • Authorization issues (IDOR/BOLA/BFLA)
   • Privilege escalation
   • Broken access control
   • Session management
   • JWT implementation
   • OAuth implementation
   • API security
   • Input validation
   • SQL injection
   • NoSQL injection
   • LDAP injection
   • Command injection
   • Path traversal
   • SSRF
   • XXE
   • Template injection
   • Deserialization
   • RCE vectors
   • File upload vulnerabilities
   • Race conditions
   • Business logic flaws
   • XSS (stored, reflected, DOM)
   • CSRF
   • CORS misconfiguration
   • Clickjacking
   • CSP weaknesses
   • Cache poisoning
   • Open redirects
   • Host header attacks
   • HTTP request smuggling
   • HTTP response splitting
   • Header security
   • Cookie security
   • Information disclosure
   • Sensitive logging
   • Secrets exposure
   • Environment variable leaks
   • Cryptographic misuse
   • Weak randomness
   • Insecure password storage
   • Timing attacks
   • DoS vectors
   • Memory exhaustion
   • Rate limiting
   • Abuse scenarios
   • Dependency vulnerabilities
   • Supply-chain risks
   • CI/CD security
   • Docker security
   • Kubernetes security
   • Infrastructure-as-Code issues
   • Cloud IAM misconfiguration
   • S3/blob storage exposure
   • SSR/CSR security
   • GraphQL issues
   • WebSocket security
   • Mobile API issues
   • Multi-tenant isolation
   • Data validation
   • Data integrity
   • Replay attacks
   • Webhook verification
   • Feature flag abuse
   • Debug endpoints
   • Admin functionality exposure
   • Hidden attack surfaces

   ---

   Threat Modeling

   For every major component:

   • Identify assets
   • Trust boundaries
   • Entry points
   • Attack surface
   • Threat actors
   • Abuse cases
   • Lateral movement paths
   • Privilege escalation paths

   ---

   Code Review

   Inspect every function.

   Identify:

   • insecure assumptions
   • unsafe defaults
   • missing validation
   • dangerous APIs
   • unsafe libraries
   • insecure patterns
   • dead code
   • forgotten endpoints
   • debug code
   • TODOs that introduce risk
   • feature flags affecting security

   ---

   Data Flow Analysis

   Trace:

   • user input
   • authentication state
   • authorization decisions
   • file uploads
   • secrets
   • tokens
   • credentials
   • cookies
   • headers
   • API requests
   • database queries

   Follow each flow from source to sink.

   ---

   Exploitability

   For every issue provide:

   Severity:

   • Critical
   • High
   • Medium
   • Low
   • Informational

   Explain:

   • why it exists
   • affected files
   • affected functions
   • exploit scenario
   • attacker prerequisites
   • business impact
   • likelihood
   • CVSS-style reasoning

   ---

   Proof of Concept

   Where appropriate provide:

   • HTTP requests
   • curl commands
   • API calls
   • payloads
   • reproduction steps
   • expected vulnerable behavior

   Only demonstrate exploitation against the provided code. Do not fabricate vulnerabilities or assume missing code behaves insecurely.

   ---

   Remediation

   For every finding include:

   • secure implementation
   • code example
   • architectural improvement
   • defense-in-depth recommendations
   • relevant security standards (OWASP ASVS, OWASP Top 10, CWE, CAPEC, NIST SSDF) where applicable

   ---

   Secure Coding Review

   Identify:

   • duplicated security logic
   • missing centralized authorization
   • inconsistent validation
   • cryptographic misuse
   • insecure error handling
   • excessive permissions
   • missing audit logging
   • excessive logging of sensitive data

   ---

   Dependency Review

   Inspect:

   • lockfiles
   • package manifests
   • transitive dependencies
   • outdated libraries
   • known vulnerable packages
   • unnecessary dependencies
   • abandoned packages

   If version information is unavailable, clearly state that vulnerability status cannot be confirmed.

   ---

   Configuration Review

   Review:

   • Dockerfiles
   • docker-compose
   • Kubernetes manifests
   • GitHub Actions
   • GitLab CI
   • Terraform
   • Helm charts
   • Nginx
   • Apache
   • Env files
   • Reverse proxies
   • Cloud configs

   ---

   Authentication Review

   Inspect:

   • login flow
   • password reset
   • MFA
   • session expiration
   • refresh tokens
   • JWT validation
   • logout
   • remember-me
   • account recovery
   • lockout policy

   ---

   Authorization Review

   Verify every endpoint enforces authorization independently. Do not assume UI restrictions provide security.

   Check for:

   • IDOR/BOLA
   • horizontal privilege escalation
   • vertical privilege escalation
   • missing ownership checks
   • tenant isolation failures

   ---

   API Review

   Inspect every endpoint for:

   • authentication
   • authorization
   • rate limiting
   • mass assignment
   • excessive data exposure
   • schema validation
   • unsafe serialization

   ---

   Final Report

   Produce:

   1. Executive summary
   2. Attack surface overview
   3. Critical findings first
   4. Detailed findings
   5. Risk matrix
   6. Prioritized remediation roadmap
   7. Positive security observations
   8. Remaining uncertainties due to missing context

   If evidence is insufficient to support a finding, explicitly mark it as a hypothesis or an area requiring manual verification rather than presenting it as a confirmed vulnerability.

   ---

   A few practical tips:

   • Audit in chunks. Large codebases often exceed a model's context window. Reviewing one service, package, or directory at a time generally yields better results than pasting an entire repository.
   • Provide architecture context. Include a high-level overview, technology stack, authentication model, deployment environment, and trust boundaries so the audit can reason about security in context.
   • Ask for evidence. Require the reviewer to cite the exact file, function, and code path supporting each finding. This reduces false positives.
   • Differentiate certainty. Have the reviewer label findings as confirmed, likely, possible, or needs verification based on the available code.
   • Use multiple passes. One pass can focus on authentication and authorization, another on injection and input validation, another on infrastructure and secrets, and a final pass on business logic and threat modeling. This often produces a more thorough review than a single broad scan.

   

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   Claude keeps blocking risky prompts. How to phrase for real audit?

   If you're using an AI to audit your own codebase, the most effective approach is to make it clear that the task is defensive security analysis and to ask for evidence-based findings. You don't need to disguise the request or try to bypass safety mechanisms.

   A prompt like this is usually both effective and aligned with the intended use of these models:

   You are a senior application security engineer performing a defensive security review of a codebase that I own or am authorized to assess.

   Review the provided code for vulnerabilities, insecure design, misconfigurations, authentication and authorization flaws, injection risks, insecure cryptography, secrets handling, business logic issues, dependency risks, and infrastructure concerns.

   Base every finding only on the code and configuration provided. Do not speculate. For each issue, include:

   • Severity and confidence
   • Exact file(s) and line(s)
   • Root cause
   • Exploitation prerequisites
   • Potential impact
   • Proof based on the code
   • Recommended remediation with example code
   • Relevant CWE and OWASP categories where applicable

   If you are uncertain, state what additional code or context is needed rather than assuming a vulnerability exists.

   Continue reviewing until every provided file has been analyzed.

   You can also improve results by:

   • Auditing one module or service at a time instead of an entire large repository.
   • Including architecture diagrams or a description of the authentication and deployment model.
   • Asking for separate passes (e.g., authorization, injection, cryptography, infrastructure, business logic).
   • Following up on each finding with: "Show me the complete execution path that leads to this conclusion."

   If your goal is specifically to get around another model's safeguards or restrictions, I can't help with crafting prompts to bypass them. If that model is declining a legitimate security review of code you own, I can help reword the request to make the defensive intent clearer or help you perform the audit here.

   AI-assisted audit vs manual pentest coverage comparison?

   AI-assisted code audits and manual penetration tests overlap, but they answer different questions. The strongest security assessments combine both rather than treating one as a replacement for the other.

   Area	AI-assisted code audit	Manual penetration test
   Source code review	Excellent (if the code is provided)	Usually limited
   Authentication/authorization logic	Good to excellent	Good, based on observable behavior
   Injection vulnerabilities	Good at identifying risky patterns	Good at verifying exploitability
   Business logic flaws	Moderate; depends on context	Often stronger due to creative testing
   Configuration review	Good if configs are available	Good if the environment is accessible
   Infrastructure issues	Limited to what is shared	Stronger when testing deployed systems
   Runtime behavior	Limited	Excellent
   Chained attack paths	Moderate	Strong
   False positives	Can be moderate	Typically fewer after validation
   Unknown/zero-day issues	Limited	Possible, but still uncommon
   Scalability	Very high	Lower, constrained by time and scope

   Where AI excels

   AI can rapidly review:

   • Large amounts of source code.
   • Repeated insecure coding patterns.
   • Missing input validation and authorization checks.
   • Insecure API usage.
   • Hardcoded secrets and credentials.
   • Risky dependency usage.
   • Configuration files (Docker, IaC, CI/CD, etc.).
   • Secure coding best practices.

   It also makes it practical to revisit the audit after every major code change.

   Where manual pentesting excels

   A skilled human tester is better at:

   • Discovering unexpected attack chains across multiple features.
   • Exploiting subtle business logic flaws.
   • Assessing how an application behaves under real-world conditions.
   • Finding vulnerabilities that only appear in the deployed environment.
   • Validating whether a suspected issue is actually exploitable.
   • Adapting creatively when initial attack paths fail.

   Typical coverage

   These figures are only rough estimates because they depend heavily on the application, the quality of the AI workflow, and the experience of the human tester.

   • AI-assisted code audit: Can provide broad coverage of the provided codebase, especially for common implementation flaws and secure coding issues.
   • Manual penetration test: Usually provides deeper validation of a defined attack surface and can uncover issues that don't appear directly in the code.
   • Combined approach: Generally offers the best overall coverage by using AI to accelerate review and humans to validate findings and explore complex attack scenarios.

   Recommended workflow

   1. Run an AI-assisted review on the entire codebase and configuration.
   2. Fix or triage the identified issues.
   3. Repeat the AI review after changes.
   4. Conduct a manual penetration test against the running application.
   5. Re-test after remediation to confirm fixes.

   Using AI as a first-pass reviewer and a human as the final validator tends to provide better coverage than relying exclusively on either approach.

   

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