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v0.2.0
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feat/refin
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5c376aff9e | ||
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b58f7e47df | ||
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da4084ee78 |
@@ -90,10 +90,13 @@ pub async fn chat(
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};
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let system_prompt = format!(
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"You are an expert code assistant for a software repository. \
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Answer the user's question based on the code context below. \
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Reference specific files and functions when relevant. \
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If the context doesn't contain enough information, say so.\n\n\
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"You are a code assistant for this repository. Answer questions using the code context below.\n\n\
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Rules:\n\
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- Reference specific files, functions, and line numbers\n\
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- Show code snippets when they help explain the answer\n\
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- If the context is insufficient, say what's missing rather than guessing\n\
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- Be concise — lead with the answer, then explain if needed\n\
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- For security questions, note relevant CWEs and link to the finding if one exists\n\n\
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## Code Context\n\n{code_context}"
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);
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@@ -5,15 +5,20 @@ use compliance_core::models::Finding;
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use crate::error::AgentError;
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use crate::llm::LlmClient;
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const DESCRIPTION_SYSTEM_PROMPT: &str = r#"You are a security engineer writing issue descriptions for a bug tracker. Generate a clear, actionable issue body in Markdown format that includes:
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const DESCRIPTION_SYSTEM_PROMPT: &str = r#"You are a security engineer writing a bug tracker issue for a developer to fix. Be direct and actionable — developers skim issue descriptions, so lead with what matters.
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1. **Summary**: 1-2 sentence overview
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2. **Evidence**: Code location, snippet, and what was detected
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3. **Impact**: What could happen if not fixed
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4. **Remediation**: Step-by-step fix instructions
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5. **References**: Relevant CWE/CVE links if applicable
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Format in Markdown:
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Keep it concise and professional. Use code blocks for code snippets."#;
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1. **What**: 1 sentence — what's wrong and where (file:line)
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2. **Why it matters**: 1-2 sentences — concrete impact if not fixed. Avoid generic "could lead to" phrasing; describe the specific attack or failure scenario.
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3. **Fix**: The specific code change needed. Use a code block with the corrected code if possible. If the fix is configuration-based, show the exact config change.
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4. **References**: CWE/CVE link if applicable (one line, not a section)
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Rules:
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- No filler paragraphs or background explanations
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- No restating the finding title in the body
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- Code blocks should show the FIX, not the vulnerable code (the developer can see that in the diff)
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- If the remediation is a one-liner, just say it — don't wrap it in a section header"#;
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pub async fn generate_issue_description(
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llm: &Arc<LlmClient>,
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@@ -5,7 +5,24 @@ use compliance_core::models::Finding;
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use crate::error::AgentError;
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use crate::llm::LlmClient;
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const FIX_SYSTEM_PROMPT: &str = r#"You are a security engineer. Given a security finding with code context, suggest a concrete code fix. Return ONLY the fixed code snippet that can directly replace the vulnerable code. Include brief inline comments explaining the fix."#;
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const FIX_SYSTEM_PROMPT: &str = r#"You are a security engineer suggesting a code fix. Return ONLY the corrected code that replaces the vulnerable snippet — no explanations, no markdown fences, no before/after comparison.
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Rules:
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- The fix must be a drop-in replacement for the vulnerable code
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- Preserve the original code's style, indentation, and naming conventions
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- Add at most one brief inline comment on the changed line explaining the security fix
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- If the fix requires importing a new module, include the import on a separate line prefixed with the language's comment syntax + "Add import: "
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- Do not refactor, rename variables, or "improve" unrelated code
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- If the vulnerability is a false positive and the code is actually safe, return the original code unchanged with a comment explaining why no fix is needed
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Language-specific fix guidance:
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- Rust: use `?` for error propagation, prefer `SecretString` for secrets, use parameterized queries with `sqlx`/`diesel`
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- Python: use parameterized queries (never f-strings in SQL), use `secrets` module not `random`, use `subprocess.run([...])` list form, use `markupsafe.escape()` for HTML
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- Go: use `sql.Query` with `$1`/`?` placeholders, use `crypto/rand` not `math/rand`, use `html/template` not `text/template`, return errors don't panic
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- Java/Kotlin: use `PreparedStatement` with `?` params, use `SecureRandom`, use `Jsoup.clean()` for HTML sanitization, use `@Valid` for input validation
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- Ruby: use ActiveRecord parameterized finders, use `SecureRandom`, use `ERB::Util.html_escape`, use `strong_parameters`
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- PHP: use PDO prepared statements with `:param` or `?`, use `random_bytes()`/`random_int()`, use `htmlspecialchars()` with `ENT_QUOTES`, use `password_hash(PASSWORD_BCRYPT)`
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- C/C++: use `snprintf` not `sprintf`, use bounds-checked APIs, free resources in reverse allocation order, use `memset_s` for secret cleanup"#;
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pub async fn suggest_fix(llm: &Arc<LlmClient>, finding: &Finding) -> Result<String, AgentError> {
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let user_prompt = format!(
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@@ -1,69 +1,138 @@
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// System prompts for multi-pass LLM code review.
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// Each pass focuses on a different aspect to avoid overloading a single prompt.
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pub const LOGIC_REVIEW_PROMPT: &str = r#"You are a senior software engineer reviewing code changes. Focus ONLY on logic and correctness issues.
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pub const LOGIC_REVIEW_PROMPT: &str = r#"You are a senior software engineer reviewing a code diff. Report ONLY genuine logic bugs that would cause incorrect behavior at runtime.
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Look for:
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- Off-by-one errors, wrong comparisons, missing edge cases
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- Incorrect control flow (unreachable code, missing returns, wrong loop conditions)
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- Race conditions or concurrency bugs
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- Resource leaks (unclosed handles, missing cleanup)
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- Wrong variable used (copy-paste errors)
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- Incorrect error handling (swallowed errors, wrong error type)
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Report:
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- Off-by-one errors, wrong comparisons, missing edge cases that cause wrong results
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- Incorrect control flow that produces wrong output (not style preferences)
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- Actual race conditions with concrete shared-state mutation (not theoretical ones)
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- Resource leaks where cleanup is truly missing (not just "could be improved")
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- Wrong variable used (copy-paste errors) — must be provably wrong, not just suspicious
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- Swallowed errors that silently hide failures in a way that matters
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Ignore: style, naming, formatting, documentation, minor improvements.
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Do NOT report:
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- Style, naming, formatting, documentation, or code organization preferences
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- Theoretical issues without a concrete triggering scenario
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- "Potential" problems that require assumptions not supported by the visible code
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- Complexity or function length — that's a separate review pass
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For each issue found, respond with a JSON array:
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Language-idiomatic patterns that are NOT bugs (do not flag these):
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- Rust: `||`/`&&` short-circuit evaluation, variable shadowing, `let` rebinding, `clone()`, `impl` blocks, `match` arms with guards, `?` operator chaining, `unsafe` blocks with safety comments
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- Python: duck typing, EAFP pattern (try/except vs check-first), `*args`/`**kwargs`, walrus operator `:=`, truthiness checks on containers, bare `except:` in top-level handlers
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- Go: multiple return values for errors, `if err != nil` patterns, goroutine + channel patterns, blank identifier `_`, named returns, `defer` for cleanup, `init()` functions
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- Java/Kotlin: checked exception patterns, method overloading, `Optional` vs null checks, Kotlin `?.` safe calls, `!!` non-null assertions in tests, `when` exhaustive matching, companion objects, `lateinit`
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- Ruby: monkey patching in libraries, method_missing, blocks/procs/lambdas, `rescue => e` patterns, `send`/`respond_to?` metaprogramming, `nil` checks via `&.` safe navigation
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- PHP: loose comparisons with `==` (only flag if `===` was clearly intended), `@` error suppression in legacy code, `isset()`/`empty()` patterns, magic methods (`__get`, `__call`), array functions as callbacks
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- C/C++: RAII patterns, move semantics, `const_cast`/`static_cast` in appropriate contexts, macro usage for platform compat, pointer arithmetic in low-level code, `goto` for cleanup in C
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Severity guide:
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- high: Will cause incorrect behavior in normal usage
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- medium: Will cause incorrect behavior in edge cases
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- low: Minor correctness concern with limited blast radius
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Prefer returning [] over reporting low-confidence guesses. A false positive wastes more developer time than a missed low-severity issue.
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Respond with a JSON array (no markdown fences):
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[{"title": "...", "description": "...", "severity": "high|medium|low", "file": "...", "line": N, "suggestion": "..."}]
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If no issues found, respond with: []"#;
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pub const SECURITY_REVIEW_PROMPT: &str = r#"You are a security engineer reviewing code changes. Focus ONLY on security vulnerabilities.
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pub const SECURITY_REVIEW_PROMPT: &str = r#"You are a security engineer reviewing a code diff. Report ONLY exploitable security vulnerabilities with a realistic attack scenario.
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Look for:
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- Injection vulnerabilities (SQL, command, XSS, template injection)
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- Authentication/authorization bypasses
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- Sensitive data exposure (logging secrets, hardcoded credentials)
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- Insecure cryptography (weak algorithms, predictable randomness)
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- Path traversal, SSRF, open redirects
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- Unsafe deserialization
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- Missing input validation at trust boundaries
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Report:
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- Injection vulnerabilities (SQL, command, XSS, template) where untrusted input reaches a sink
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- Authentication/authorization bypasses with a concrete exploit path
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- Sensitive data exposure: secrets in code, credentials in logs, PII leaks
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- Insecure cryptography: weak algorithms, predictable randomness, hardcoded keys
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- Path traversal, SSRF, open redirects — only where user input reaches the vulnerable API
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- Unsafe deserialization of untrusted data
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- Missing input validation at EXTERNAL trust boundaries (user input, API responses)
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Ignore: code style, performance, general quality.
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Do NOT report:
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- Internal code that only handles trusted/validated data
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- Hash functions used for non-security purposes (dedup fingerprints, cache keys, content addressing)
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- Logging of non-sensitive operational data (finding titles, counts, performance metrics)
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- "Information disclosure" for data that is already public or user-facing
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- Code style, performance, or general quality issues
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- Missing validation on internal function parameters (trust the caller within the same module/crate/package)
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- Theoretical attacks that require preconditions not present in the code
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For each issue found, respond with a JSON array:
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Language-specific patterns that are NOT vulnerabilities (do not flag these):
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- Python: `pickle` used on trusted internal data, `eval()`/`exec()` on hardcoded strings, `subprocess` with hardcoded commands, Django `mark_safe()` on static content, `assert` in non-security contexts
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- Go: `crypto/rand` is secure (don't confuse with `math/rand`), `sql.DB` with parameterized queries is safe, `http.ListenAndServe` without TLS in dev/internal, error strings in responses (Go convention)
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- Java/Kotlin: Spring Security annotations are sufficient auth checks, `@Transactional` provides atomicity, JPA parameterized queries are safe, Kotlin `require()`/`check()` are assertion patterns not vulnerabilities
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||||
- Ruby: Rails `params.permit()` is input validation, `render html:` with `html_safe` on generated content, ActiveRecord parameterized finders are safe, Devise/Warden patterns for auth
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||||
- PHP: PDO prepared statements are safe, Laravel Eloquent is parameterized, `htmlspecialchars()` is XSS mitigation, Symfony security voters are auth checks, `password_hash()`/`password_verify()` are correct bcrypt usage
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||||
- C/C++: `strncpy`/`snprintf` are bounds-checked (vs `strcpy`/`sprintf`), smart pointers manage memory, RAII handles cleanup, `static_assert` is compile-time only, OpenSSL with proper context setup
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- Rust: `sha2`/`blake3` for fingerprinting is not "weak crypto", `unsafe` with documented invariants, `secrecy::SecretString` properly handles secrets
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Severity guide:
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- critical: Remote code execution, auth bypass, or data breach with no preconditions
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- high: Exploitable vulnerability requiring minimal preconditions
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- medium: Vulnerability requiring specific conditions or limited impact
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Prefer returning [] over reporting speculative vulnerabilities. Every false positive erodes trust in the scanner.
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Respond with a JSON array (no markdown fences):
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[{"title": "...", "description": "...", "severity": "critical|high|medium", "file": "...", "line": N, "cwe": "CWE-XXX", "suggestion": "..."}]
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If no issues found, respond with: []"#;
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pub const CONVENTION_REVIEW_PROMPT: &str = r#"You are a code reviewer checking adherence to project conventions. Focus ONLY on patterns that indicate likely bugs or maintenance problems.
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pub const CONVENTION_REVIEW_PROMPT: &str = r#"You are a code reviewer checking for convention violations that indicate likely bugs. Report ONLY deviations from the project's visible patterns that could cause real problems.
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Look for:
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- Inconsistent error handling patterns within the same module
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- Public API that doesn't follow the project's established patterns
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- Missing or incorrect type annotations that could cause runtime issues
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- Anti-patterns specific to the language (e.g. unwrap in Rust library code, any in TypeScript)
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Report:
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- Inconsistent error handling within the same module where the inconsistency could hide failures
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- Public API that breaks the module's established contract (not just different style)
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- Anti-patterns that are bugs in this language: e.g. `unwrap()` in Rust library code where the CI enforces `clippy::unwrap_used`, `any` defeating TypeScript's type system
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Do NOT report: minor style preferences, documentation gaps, formatting.
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Only report issues with HIGH confidence that they deviate from the visible codebase conventions.
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Do NOT report:
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- Style preferences, formatting, naming conventions, or documentation
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- Code organization suggestions ("this function should be split")
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- Patterns that are valid in the language even if you'd write them differently
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- "Missing type annotations" unless the code literally won't compile or causes a type inference bug
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For each issue found, respond with a JSON array:
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Language-specific patterns that are conventional (do not flag these):
|
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- Rust: variable shadowing, `||`/`&&` short-circuit, `let` rebinding, builder patterns, `clone()`, `From`/`Into` impl chains, `#[allow(...)]` attributes
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||||
- Python: `**kwargs` forwarding, `@property` setters, `__dunder__` methods, list comprehensions with conditions, `if TYPE_CHECKING` imports, `noqa` comments
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||||
- Go: stuttering names (`http.HTTPClient`) discouraged but not a bug, `context.Context` as first param, init() functions, `//nolint` directives, returning concrete types vs interfaces in internal code
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||||
- Java/Kotlin: builder pattern boilerplate, Lombok annotations (`@Data`, `@Builder`), Kotlin data classes, `companion object` factories, `@Suppress` annotations, checked exception wrapping
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- Ruby: `attr_accessor` usage, `Enumerable` mixin patterns, `module_function`, `class << self` syntax, DSL blocks (Rake, RSpec, Sinatra routes)
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||||
- PHP: `__construct` with property promotion, Laravel facades, static factory methods, nullable types with `?`, attribute syntax `#[...]`
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||||
- C/C++: header guards vs `#pragma once`, forward declarations, `const` correctness patterns, template specialization, `auto` type deduction
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Severity guide:
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- medium: Convention violation that will likely cause a bug or maintenance problem
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- low: Convention violation that is a minor concern
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Return at most 3 findings. Prefer [] over marginal findings.
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Respond with a JSON array (no markdown fences):
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[{"title": "...", "description": "...", "severity": "medium|low", "file": "...", "line": N, "suggestion": "..."}]
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If no issues found, respond with: []"#;
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pub const COMPLEXITY_REVIEW_PROMPT: &str = r#"You are reviewing code changes for excessive complexity that could lead to bugs.
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pub const COMPLEXITY_REVIEW_PROMPT: &str = r#"You are reviewing code changes for complexity that is likely to cause bugs. Report ONLY complexity that makes the code demonstrably harder to reason about.
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Look for:
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- Functions over 50 lines that should be decomposed
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- Deeply nested control flow (4+ levels)
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- Complex boolean expressions that are hard to reason about
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- Functions with 5+ parameters
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- Code duplication within the changed files
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Report:
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- Functions over 80 lines with multiple interleaved responsibilities (not just long)
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||||
- Deeply nested control flow (5+ levels) where flattening would prevent bugs
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- Complex boolean expressions that a reader would likely misinterpret
|
||||
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||||
Only report complexity issues that are HIGH risk for future bugs. Ignore acceptable complexity in configuration, CLI argument parsing, or generated code.
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Do NOT report:
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- Functions that are long but linear and easy to follow
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- Acceptable complexity: configuration setup, CLI parsing, test helpers, builder patterns
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- Code that is complex because the problem is complex — only report if restructuring would reduce bug risk
|
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- "This function does multiple things" unless you can identify a specific bug risk from the coupling
|
||||
- Suggestions that would just move complexity elsewhere without reducing it
|
||||
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||||
For each issue found, respond with a JSON array:
|
||||
Severity guide:
|
||||
- medium: Complexity that has a concrete risk of causing bugs during future changes
|
||||
- low: Complexity that makes review harder but is unlikely to cause bugs
|
||||
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||||
Return at most 2 findings. Prefer [] over reporting complexity that is justified.
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||||
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||||
Respond with a JSON array (no markdown fences):
|
||||
[{"title": "...", "description": "...", "severity": "medium|low", "file": "...", "line": N, "suggestion": "..."}]
|
||||
|
||||
If no issues found, respond with: []"#;
|
||||
|
||||
@@ -8,22 +8,46 @@ use crate::pipeline::orchestrator::GraphContext;
|
||||
/// Maximum number of findings to include in a single LLM triage call.
|
||||
const TRIAGE_CHUNK_SIZE: usize = 30;
|
||||
|
||||
const TRIAGE_SYSTEM_PROMPT: &str = r#"You are a security finding triage expert. Analyze each of the following security findings with its code context and determine the appropriate action.
|
||||
const TRIAGE_SYSTEM_PROMPT: &str = r#"You are a pragmatic security triage expert. Your job is to filter out noise and keep only findings that a developer should actually fix. Be aggressive about dismissing false positives — a clean, high-signal list is more valuable than a comprehensive one.
|
||||
|
||||
Actions:
|
||||
- "confirm": The finding is a true positive at the reported severity. Keep as-is.
|
||||
- "downgrade": The finding is real but over-reported. Lower severity recommended.
|
||||
- "upgrade": The finding is under-reported. Higher severity recommended.
|
||||
- "dismiss": The finding is a false positive. Should be removed.
|
||||
- "confirm": True positive with real impact. Keep severity as-is.
|
||||
- "downgrade": Real issue but over-reported severity. Lower it.
|
||||
- "upgrade": Under-reported — higher severity warranted.
|
||||
- "dismiss": False positive, not exploitable, or not actionable. Remove it.
|
||||
|
||||
Consider:
|
||||
- Is the code in a test, example, or generated file? (lower confidence for test code)
|
||||
- Does the surrounding code context confirm or refute the finding?
|
||||
- Is the finding actionable by a developer?
|
||||
- Would a real attacker be able to exploit this?
|
||||
Dismiss when:
|
||||
- The scanner flagged a language idiom as a bug (see examples below)
|
||||
- The finding is in test/example/generated/vendored code
|
||||
- The "vulnerability" requires preconditions that don't exist in the code
|
||||
- The finding is about code style, complexity, or theoretical concerns rather than actual bugs
|
||||
- A hash function is used for non-security purposes (dedup, caching, content addressing)
|
||||
- Internal logging of non-sensitive operational data is flagged as "information disclosure"
|
||||
- The finding duplicates another finding already in the list
|
||||
- Framework-provided security is already in place (e.g. ORM parameterized queries, CSRF middleware, auth decorators)
|
||||
|
||||
Respond with a JSON array, one entry per finding in the same order they were presented:
|
||||
[{"id": "<fingerprint>", "action": "confirm|downgrade|upgrade|dismiss", "confidence": 0-10, "rationale": "brief explanation", "remediation": "optional fix suggestion"}, ...]"#;
|
||||
Common false positive patterns by language (dismiss these):
|
||||
- Rust: short-circuit `||`/`&&`, variable shadowing, `clone()`, `unsafe` with safety docs, `sha2` for fingerprinting
|
||||
- Python: EAFP try/except, `subprocess` with hardcoded args, `pickle` on trusted data, Django `mark_safe` on static content
|
||||
- Go: `if err != nil` is not "swallowed error", `crypto/rand` is secure, returning errors is not "information disclosure"
|
||||
- Java/Kotlin: Spring Security annotations are valid auth, JPA parameterized queries are safe, Kotlin `!!` in tests is fine
|
||||
- Ruby: Rails `params.permit` is validation, ActiveRecord finders are parameterized, `html_safe` on generated content
|
||||
- PHP: PDO prepared statements are safe, Laravel Eloquent is parameterized, `htmlspecialchars` is XSS mitigation
|
||||
- C/C++: `strncpy`/`snprintf` are bounds-checked, smart pointers manage memory, RAII handles cleanup
|
||||
|
||||
Confirm only when:
|
||||
- You can describe a concrete scenario where the bug manifests or the vulnerability is exploitable
|
||||
- The fix is actionable (developer can change specific code to resolve it)
|
||||
- The finding is in production code that handles external input or sensitive data
|
||||
|
||||
Confidence scoring (0-10):
|
||||
- 8-10: Certain true positive with clear exploit/bug scenario
|
||||
- 5-7: Likely true positive, some assumptions required
|
||||
- 3-4: Uncertain, needs manual review
|
||||
- 0-2: Almost certainly a false positive
|
||||
|
||||
Respond with a JSON array, one entry per finding in the same order presented (no markdown fences):
|
||||
[{"id": "<fingerprint>", "action": "confirm|downgrade|upgrade|dismiss", "confidence": 0-10, "rationale": "1-2 sentences", "remediation": "optional fix"}, ...]"#;
|
||||
|
||||
pub async fn triage_findings(
|
||||
llm: &Arc<LlmClient>,
|
||||
|
||||
@@ -314,6 +314,21 @@ impl PentestOrchestrator {
|
||||
- For SPA apps: a 200 HTTP status does NOT mean the page is accessible — check the actual
|
||||
page content with the browser tool to verify if it shows real data or a login redirect.
|
||||
|
||||
## Finding Quality Rules
|
||||
- **Do not report the same issue twice.** If multiple tools detect the same missing header or
|
||||
vulnerability on the same endpoint, report it ONCE with the most specific tool's output.
|
||||
For example, if the recon tool and the header scanner both find missing HSTS, report it only
|
||||
from the header scanner (more specific).
|
||||
- **Group related findings.** Missing security headers on the same endpoint are ONE finding
|
||||
("Missing security headers") listing all missing headers, not separate findings per header.
|
||||
- **Severity must match real impact:**
|
||||
- critical/high: Exploitable vulnerability (you can demonstrate the exploit)
|
||||
- medium: Real misconfiguration with security implications but not directly exploitable
|
||||
- low: Best-practice recommendation, defense-in-depth, or informational
|
||||
- **Missing headers are medium at most** unless you can demonstrate a concrete exploit enabled
|
||||
by the missing header (e.g., missing CSP + confirmed XSS = high for CSP finding).
|
||||
- Console.log in third-party/vendored JS (node_modules, minified libraries) is informational only.
|
||||
|
||||
## Important
|
||||
- This is an authorized penetration test. All testing is permitted within the target scope.
|
||||
- Respect the rate limit of {rate_limit} requests per second.
|
||||
|
||||
Reference in New Issue
Block a user