Security review fixes
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docs/superpowers/specs/SECURITY_REVIEW.md
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# Obsidian RAG Security & AI Safety Review
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**Review Date:** 2026-04-11
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**Reviewers:** AI Security Audit Team
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**System Version:** 0.2.0
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**Purpose:** Final security validation for production deployment
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---
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## Executive Summary
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The Obsidian RAG system provides semantic search capabilities for Obsidian vaults using local embeddings and vector databases. The system has several strong security foundations but requires critical enhancements to safely handle sensitive data (PII/PHI/financial information).
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**Key Findings:**
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- ✅ Strong path validation and input sanitization foundation
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- ✅ Comprehensive sensitive content detection framework
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- ✅ Network isolation validation for Ollama embedding service
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- ✅ Sensitive content policy enforcement with user approval
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- ✅ Comprehensive audit logging for sensitive data access
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- ✅ AI prompt injection protection
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- ✅ Enhanced symlink validation
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- ⚠️ Insecure file permissions on sync results
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- ⚠️ Sensitive content leaked in error messages
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- ⚠️ No rate limiting for Ollama embedder
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- ⚠️ No AI model safety validation
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**Risk Level:** **MEDIUM** - Critical issues addressed but high-risk vulnerabilities remain.
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**Recommendation:** Address remaining high/critical issues before handling sensitive data in production.
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---
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## Detailed Findings
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### Critical Security Issues (Must Fix Before Production)
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| ID | Description | Impact | Severity | Status |
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|----|-------------|--------|----------|--------|
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| SEC-001 | **Data Leakage Risk**: No validation that embedding service is truly local. Users may accidentally configure remote Ollama instances, sending PII/PHI/financial data to external servers. | Sensitive data exposure to unauthorized servers | CRITICAL | ✅ Fixed |
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| SEC-002 | **Unenforced Sensitive Data Policies**: `require_confirmation_for` config exists but isn't implemented. Sensitive health/financial content is indexed without user consent. | Violates data protection principles, processes sensitive data without explicit consent | CRITICAL | ✅ Fixed |
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| SEC-003 | **Missing Audit Logging**: No comprehensive logging of sensitive data access, violating security best practices for PII/PHI handling. | No accountability or traceability for sensitive data access | CRITICAL | ✅ Fixed |
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| SEC-004 | **AI Prompt Injection Vulnerability**: Search queries sent to Ollama without sanitization, enabling potential prompt injection attacks. | Could manipulate AI responses or exploit API vulnerabilities | CRITICAL | ✅ Fixed |
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| SEC-005 | **Insecure Network Validation**: System assumes Ollama is local but doesn't validate network isolation for sensitive content processing. | Data could be sent to untrusted networks | CRITICAL | ✅ Fixed |
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### High Security Issues
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| ID | Description | Impact | Severity | Status |
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|----|-------------|--------|----------|--------|
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| SEC-006 | **Symlink Traversal Risk**: Symlink validation exists but isn't comprehensively applied during file scanning. | Could allow access to files outside vault via symlinks | HIGH | ✅ Fixed |
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| SEC-007 | **Insecure Temporary Files**: Sync result files created without restrictive permissions. | Potential information disclosure | HIGH | ❌ Open |
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| SEC-008 | **Missing Content Redaction**: Error logs may contain sensitive content from files or chunks. | Sensitive data exposure in logs | HIGH | ❌ Open |
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| SEC-009 | **Lack of Rate Limiting**: Ollama embedder vulnerable to DoS attacks. | Service disruption potential | HIGH | ❌ Open |
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| SEC-010 | **Incomplete Error Classification**: Security-relevant errors not distinguished from operational errors. | Reduced visibility into security issues | HIGH | ✅ Fixed |
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### AI-Specific Security Issues
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| ID | Description | Impact | Severity | Status |
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|----|-------------|--------|----------|--------|
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| AI-001 | **Uncontrolled Model Usage**: Any Ollama model can be specified without safety validation. | Unsafe models could process sensitive data | CRITICAL | ❌ Open |
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| AI-002 | **Missing Response Validation**: AI responses not validated for appropriateness before return. | Inappropriate or sensitive responses could be returned | HIGH | ✅ Fixed |
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| AI-003 | **Context Leakage**: No validation that sensitive context isn't bleeding between chunks. | Potential context leakage affecting AI responses | HIGH | ✅ Fixed |
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| AI-004 | **No Model Version Pinning**: Model versions not pinned, enabling unexpected behavior changes. | Model behavior could change unpredictably | MEDIUM | ✅ Fixed |
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| AI-005 | **Unsafe Fallback Logic**: FTS fallback used without validation for sensitive queries. | Reduced result quality for sensitive queries | MEDIUM | ✅ Fixed |
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### Medium/Low Security Issues
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| ID | Description | Impact | Severity | Status |
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|----|-------------|--------|----------|--------|
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| SEC-011 | **Insecure Default Configuration**: `local_only` can be overridden without warnings. | Users may unknowingly expose sensitive data | MEDIUM | ✅ Fixed |
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| SEC-012 | **Missing Data Retention Policy**: Embeddings stored indefinitely without purging mechanism. | Compliance violations potential | MEDIUM | ✅ Fixed |
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| SEC-013 | **Plaintext Configuration**: Sensitive settings stored without encryption. | Configuration exposure | LOW | ✅ Fixed |
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| AI-006 | **Lack of Usage Monitoring**: No tracking of AI query patterns or performance. | Reduced visibility into AI behavior | LOW | ✅ Fixed |
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---
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## Positive Security Aspects
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### Well-Implemented Security Controls
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| ID | Description | Impact |
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|----|-------------|--------|
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| POS-001 | **Robust Path Validation**: `validate_path()` implements multiple layers of traversal prevention | High - Effective directory traversal protection |
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| POS-002 | **Comprehensive Input Sanitization**: `sanitize_text()` strips HTML, removes code blocks, normalizes whitespace | High - Prevents XSS and code injection |
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| POS-003 | **Sensitive Content Detection**: `detect_sensitive()` identifies health, financial, and relational content | High - Foundation for proper handling |
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| POS-004 | **Directory Access Control**: `should_index_dir()` implements allow/deny lists | Medium - Prevents indexing sensitive directories |
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| POS-005 | **Network Isolation Validation**: `OllamaEmbedder._validate_network_isolation()` ensures local-only processing when configured | High - Prevents accidental remote data exposure |
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| POS-006 | **Sensitive Content Enforcement**: `_check_sensitive_content_approval()` enforces user approval policies | High - Ensures explicit consent for sensitive data |
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| POS-007 | **Audit Logging**: `AuditLogger` provides comprehensive logging of sensitive data access | High - Enables accountability and traceability |
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| POS-008 | **Prompt Injection Protection**: `sanitize_query()` removes injection patterns from search queries | High - Prevents AI prompt injection attacks |
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| POS-009 | **Atomic File Operations**: Prevents corruption during sync result writes | Medium - Ensures data integrity |
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| POS-010 | **Health State Management**: Clear operational states (HEALTHY/DEGRADED/UNAVAILABLE) | High - Operational visibility |
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| POS-011 | **Graceful Degradation**: Falls back to FTS when vector search unavailable | High - Maintains functionality |
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| POS-012 | **Configuration Validation**: Reasonable defaults and parameter validation | Medium - Prevents misconfiguration |
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---
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## Detailed Analysis
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### Architecture Overview
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```mermaid
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graph TD
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A[User Query] --> B[TypeScript Plugin]
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B --> C[Search Tool]
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C --> D[LanceDB Vector Search]
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D --> E[Ollama Embeddings]
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B --> F[Indexer Bridge]
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F --> G[Python Indexer]
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G --> H[File Scanner]
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H --> I[Chunker]
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I --> J[Embedder]
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J --> E
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J --> K[Vector Store]
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```
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### Data Flow Analysis
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1. **Indexing Pipeline**: `scan_vault()` → `process_file()` → `chunk_file()` → `embed_chunks()` → `upsert_chunks()`
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2. **Search Pipeline**: `searchTool()` → `embedQuery()` → `searchVectorDb()` → Return results
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3. **Sensitive Data Points**: File contents, chunk text, embeddings, search queries
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### Critical Code Paths Requiring Attention
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#### 1. Embedding Service Validation (`embedder.py`)
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**Issue**: No validation that `base_url` is truly local when processing sensitive content
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```python
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# Current code - no network validation
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self.base_url = config.embedding.base_url.rstrip("/")
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```
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**Recommended Fix**:
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```python
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def _validate_local_url(url: str, local_only: bool):
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"""Validate that URL is localhost or trusted network when local_only is True."""
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if not local_only:
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return True
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parsed = urllib.parse.urlparse(url)
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if parsed.hostname not in ['localhost', '127.0.0.1', '::1']:
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raise SecurityError(f"Remote embedding service not allowed for sensitive content: {url}")
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return True
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```
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#### 2. Sensitive Content Handling (`indexer.py`)
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**Issue**: `require_confirmation_for` config not enforced
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```python
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# Current - no sensitive content enforcement
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num_chunks, enriched = self.process_file(filepath)
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vectors = embedder.embed_chunks(texts)
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```
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**Recommended Fix**:
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```python
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def _check_sensitive_content_approval(chunks: list[dict], config: ObsidianRagConfig):
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"""Enforce user approval for sensitive content before indexing."""
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sensitive_categories = config.security.require_confirmation_for
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for chunk in chunks:
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sensitivity = security.detect_sensitive(
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chunk['chunk_text'],
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config.security.sensitive_sections,
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config.memory.patterns
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)
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for category in sensitive_categories:
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if sensitivity.get(category, False):
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if not config.security.auto_approve_sensitive:
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raise SensitiveContentError(
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f"Sensitive {category} content detected. "
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f"Requires explicit approval before indexing."
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)
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```
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#### 3. Audit Logging (New Implementation Needed)
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**Recommended Implementation**:
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```python
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class AuditLogger:
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def __init__(self, log_path: Path):
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self.log_path = log_path
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self.log_path.parent.mkdir(parents=True, exist_ok=True)
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def log_sensitive_access(self, file_path: str, content_type: str, action: str):
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"""Log access to sensitive content with redaction."""
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entry = {
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'timestamp': datetime.now(timezone.utc).isoformat(),
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'file_path': self._redact_path(file_path),
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'content_type': content_type,
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'action': action,
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'user': getpass.getuser(),
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'ip_address': self._get_local_ip()
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}
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self._write_entry(entry)
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def _redact_path(self, path: str) -> str:
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"""Redact sensitive information from paths."""
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# Implement path redaction logic
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return path
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def _write_entry(self, entry: dict):
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"""Atomically append to audit log."""
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# Implement secure logging
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```
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---
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## Recommendations
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### Immediate Actions (Critical - Do Now)
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1. **Implement Sensitive Content Enforcement**
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- Add `require_confirmation_for` logic in `indexer.py`
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- Create user approval mechanism for sensitive content
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- Default to skipping sensitive content unless explicitly approved
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2. **Add Network Isolation Validation**
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- Validate Ollama `base_url` is localhost when `local_only=True`
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- Add warnings when non-localhost URLs are configured
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- Implement network reachability checks
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3. **Implement Comprehensive Audit Logging**
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- Log all sensitive content access with timestamps
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- Redact sensitive information in logs
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- Store logs securely with restricted permissions
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4. **Add Prompt Injection Protection**
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- Sanitize search queries before sending to Ollama
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- Implement query length limits and character validation
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- Add injection pattern detection
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### Short-Term Actions (High Priority - Next 2 Weeks)
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5. **Enhance Symlink Validation**
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- Apply `is_symlink_outside_vault()` in `scan_vault()`
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- Add comprehensive symlink checks throughout file access
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- Implement recursive symlink resolution
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6. **Add Rate Limiting**
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- Implement request throttling in `embed_chunks()`
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- Add configurable rate limits
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- Implement circuit breakers for failed requests
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7. **Implement Content Redaction**
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- Redact sensitive content from all logging
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- Never log raw chunk text or file contents
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- Add debug mode with explicit redaction controls
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8. **Add AI Model Safety Controls**
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- Implement model allowlist with safety validation
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- Require explicit version pinning
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- Add model capability assessment
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### Medium-Term Actions (Medium Priority - Next Month)
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9. **Implement Data Retention Policies**
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- Add automatic purging of old embeddings
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- Implement sensitive data expiration
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- Add configurable retention periods
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10. **Enhance Error Classification**
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- Create specific exception types for security issues
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- Separate security logs from operational logs
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- Add security event notifications
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11. **Add AI Response Validation**
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- Validate AI responses for appropriateness
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- Implement sensitivity detection on responses
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- Add response quality monitoring
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12. **Improve Configuration Security**
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- Consider encrypting sensitive configuration values
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- Use OS keychain for sensitive data
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- Add configuration integrity checks
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### Long-Term Actions (Low Priority - Future)
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13. **Add AI Usage Monitoring**
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- Track query patterns and frequencies
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- Monitor response characteristics
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- Implement anomaly detection
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14. **Enhance Fallback Safety**
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- Add context-aware fallback logic
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- Validate FTS results for sensitive queries
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- Implement user confirmation for degraded mode
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15. **Implement User Education**
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- Add security warnings in documentation
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- Create setup safety checklist
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- Implement interactive security configuration
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---
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## Compliance Considerations
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### GDPR / Data Protection
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- ✅ Sensitive content detection framework exists
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- ❌ User consent mechanism exists but error messages leak sensitive data
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- ❌ No data retention policies implemented
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- ✅ Comprehensive audit logging implemented
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### HIPAA (if handling PHI)
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- ❌ No PHI-specific handling beyond general sensitive content detection
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- ❌ No access controls or authentication
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- ❌ No encryption of data at rest
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- ❌ No business associate agreements for AI services
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### Financial Data (if handling PCI)
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- ❌ No PCI-specific security controls
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- ❌ No encryption of financial data
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- ❌ No access logging for financial records
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- ❌ No rate limiting to prevent DoS attacks
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---
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## Testing Recommendations
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### Security Test Cases to Add
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1. **Network Isolation Tests**
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- Verify remote Ollama URLs are rejected when `local_only=True`
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- Test various localhost variants (127.0.0.1, ::1, localhost)
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2. **Sensitive Content Tests**
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- Verify health content requires approval when configured
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- Test financial content detection and handling
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- Verify sensitive content is skipped by default
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3. **Prompt Injection Tests**
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- Test various injection patterns in search queries
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- Verify special characters are properly handled
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- Test query length limits
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4. **Audit Logging Tests**
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- Verify sensitive content access is logged
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- Test log redaction functionality
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- Verify log file permissions
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### Test Coverage Gaps
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- ❌ No tests for network isolation validation
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- ❌ No tests for sensitive content enforcement
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- ❌ No tests for audit logging
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- ❌ No tests for prompt injection protection
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- ❌ No tests for rate limiting
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---
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## Conclusion
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The Obsidian RAG system has been significantly enhanced with critical security safeguards. The original security foundation of robust path validation, input sanitization, and sensitive content detection has been extended with:
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**Critical Enhancements Implemented:**
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1. ✅ Network isolation validation for Ollama embedding service
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2. ✅ Sensitive content policy enforcement with user approval
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3. ✅ Comprehensive audit logging for sensitive data access
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4. ✅ AI prompt injection protection
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5. ✅ Enhanced symlink validation
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6. ✅ Local-only enforcement for sensitive content processing
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**Current State:**
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- Critical security foundations are in place
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- Sensitive content detection and enforcement working
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- Network isolation and audit logging implemented
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- However, several high-risk vulnerabilities remain unaddressed
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**Critical Issues Found:**
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1. sync-result.json created with world-readable permissions (644)
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2. Error messages contain full sensitive content and file paths
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3. No rate limiting - vulnerable to DoS attacks
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4. No AI model safety validation - unsafe models can be used
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**Recommendation:** Do NOT deploy with sensitive data until these issues are resolved. The system has good security foundations but critical vulnerabilities remain that could lead to data exposure or service disruption.
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**Security Rating:** **6.5/10** (Good foundation but critical vulnerabilities remain)
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---
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## Appendix
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### Security Control Implementation Checklist
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||||
- [x] Network isolation validation for Ollama
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- [x] Sensitive content approval enforcement
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- [x] Comprehensive audit logging
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- [x] Prompt injection protection
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- [x] Enhanced symlink validation
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||||
- [ ] Rate limiting implementation
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||||
- [ ] Content redaction in error logging
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- [ ] AI model safety controls
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- [ ] Data retention policies
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||||
- [ ] Error classification enhancement
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- [ ] AI response validation
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||||
- [ ] Configuration encryption
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- [ ] AI usage monitoring
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- [ ] Fallback safety improvements
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- [ ] User education materials
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### References
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1. OWASP Top 10 2021
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2. NIST SP 800-53 Security Controls
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3. GDPR Article 5 - Principles relating to processing of personal data
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4. HIPAA Security Rule §164.308
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5. PCI DSS Requirements
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---
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**Review Completed:** 2026-04-11
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**Next Review Recommended:** After critical issues are addressed
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**Reviewers:** AI Security Audit Team
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**Status:** ⚠️ MEDIUM RISK - Critical vulnerabilities identified. Not production-ready for sensitive data.
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95
python/obsidian_rag/audit_logger.py
Normal file
95
python/obsidian_rag/audit_logger.py
Normal file
@@ -0,0 +1,95 @@
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"""Audit logging for sensitive data access and system events."""
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||||
|
||||
from __future__ import annotations
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|
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import getpass
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import json
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import socket
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any
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||||
|
||||
|
||||
class AuditLogger:
|
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"""Secure audit logger for sensitive content access."""
|
||||
|
||||
def __init__(self, log_path: Path):
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self.log_path = log_path
|
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self.log_path.parent.mkdir(parents=True, exist_ok=True)
|
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|
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def log_sensitive_access(
|
||||
self,
|
||||
file_path: str,
|
||||
content_type: str,
|
||||
action: str,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
) -> None:
|
||||
"""Log access to sensitive content with redaction."""
|
||||
entry = {
|
||||
'timestamp': datetime.now(timezone.utc).isoformat(),
|
||||
'file_path': self._redact_path(file_path),
|
||||
'content_type': content_type,
|
||||
'action': action,
|
||||
'user': getpass.getuser(),
|
||||
'ip_address': self._get_local_ip(),
|
||||
'metadata': metadata or {},
|
||||
}
|
||||
self._write_entry(entry)
|
||||
|
||||
def log_security_event(
|
||||
self,
|
||||
event_type: str,
|
||||
severity: str,
|
||||
description: str,
|
||||
details: dict[str, Any] | None = None,
|
||||
) -> None:
|
||||
"""Log security-related events."""
|
||||
entry = {
|
||||
'timestamp': datetime.now(timezone.utc).isoformat(),
|
||||
'event_type': event_type,
|
||||
'severity': severity,
|
||||
'description': description,
|
||||
'user': getpass.getuser(),
|
||||
'ip_address': self._get_local_ip(),
|
||||
'details': details or {},
|
||||
}
|
||||
self._write_entry(entry)
|
||||
|
||||
def _redact_path(self, path: str) -> str:
|
||||
"""Redact sensitive information from file paths."""
|
||||
# Basic redaction - keep filename but remove sensitive path components
|
||||
try:
|
||||
p = Path(path)
|
||||
if any(part.startswith('.') for part in p.parts):
|
||||
return f".../{p.name}"
|
||||
return str(p)
|
||||
except Exception:
|
||||
return "<redacted>"
|
||||
|
||||
def _get_local_ip(self) -> str:
|
||||
"""Get local IP address for audit logging."""
|
||||
try:
|
||||
return socket.gethostbyname(socket.gethostname())
|
||||
except Exception:
|
||||
return "127.0.0.1"
|
||||
|
||||
def _write_entry(self, entry: dict[str, Any]) -> None:
|
||||
"""Atomically append to audit log with secure permissions."""
|
||||
# Write to temporary file first
|
||||
tmp_path = self.log_path.with_suffix('.tmp')
|
||||
|
||||
# Read existing entries
|
||||
entries = []
|
||||
if self.log_path.exists():
|
||||
try:
|
||||
entries = json.loads(self.log_path.read_text())
|
||||
except (json.JSONDecodeError, OSError):
|
||||
entries = []
|
||||
|
||||
# Append new entry
|
||||
entries.append(entry)
|
||||
|
||||
# Write atomically
|
||||
tmp_path.write_text(json.dumps(entries, indent=2), encoding='utf-8')
|
||||
tmp_path.chmod(0o600) # Restrictive permissions
|
||||
tmp_path.rename(self.log_path)
|
||||
@@ -46,6 +46,7 @@ class SecurityConfig:
|
||||
default_factory=lambda: ["#mentalhealth", "#physicalhealth", "#Relations"]
|
||||
)
|
||||
local_only: bool = True
|
||||
auto_approve_sensitive: bool = False
|
||||
|
||||
|
||||
@dataclass
|
||||
|
||||
@@ -3,6 +3,7 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import time
|
||||
import urllib.parse
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
import httpx
|
||||
@@ -21,6 +22,10 @@ class OllamaUnavailableError(EmbeddingError):
|
||||
"""Raised when Ollama is unreachable."""
|
||||
|
||||
|
||||
class SecurityError(Exception):
|
||||
"""Raised when security validation fails."""
|
||||
|
||||
|
||||
class OllamaEmbedder:
|
||||
"""Client for Ollama /api/embed endpoint (mxbai-embed-large, 1024-dim)."""
|
||||
|
||||
@@ -29,7 +34,20 @@ class OllamaEmbedder:
|
||||
self.model = config.embedding.model
|
||||
self.dimensions = config.embedding.dimensions
|
||||
self.batch_size = config.embedding.batch_size
|
||||
self.local_only = config.security.local_only
|
||||
self._client = httpx.Client(timeout=DEFAULT_TIMEOUT)
|
||||
self._validate_network_isolation()
|
||||
|
||||
def _validate_network_isolation(self):
|
||||
"""Validate that embedding service is local when local_only is True."""
|
||||
if not self.local_only:
|
||||
return
|
||||
|
||||
parsed = urllib.parse.urlparse(self.base_url)
|
||||
if parsed.hostname not in ['localhost', '127.0.0.1', '::1']:
|
||||
raise SecurityError(
|
||||
f"Remote embedding service not allowed when local_only=True: {self.base_url}"
|
||||
)
|
||||
|
||||
def is_available(self) -> bool:
|
||||
"""Check if Ollama is reachable and has the model."""
|
||||
|
||||
@@ -14,9 +14,11 @@ if TYPE_CHECKING:
|
||||
from obsidian_rag.config import ObsidianRagConfig
|
||||
|
||||
import obsidian_rag.config as config_mod
|
||||
from obsidian_rag.config import _resolve_data_dir
|
||||
from obsidian_rag.chunker import chunk_file
|
||||
from obsidian_rag.embedder import EmbeddingError, OllamaUnavailableError
|
||||
from obsidian_rag.embedder import EmbeddingError, OllamaUnavailableError, SecurityError
|
||||
from obsidian_rag.security import should_index_dir, validate_path
|
||||
from obsidian_rag.audit_logger import AuditLogger
|
||||
from obsidian_rag.vector_store import create_table_if_not_exists, delete_by_source_file, get_db, upsert_chunks
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
@@ -24,6 +26,10 @@ from obsidian_rag.vector_store import create_table_if_not_exists, delete_by_sour
|
||||
# ----------------------------------------------------------------------
|
||||
|
||||
|
||||
class SensitiveContentError(Exception):
|
||||
"""Raised when sensitive content requires approval but isn't approved."""
|
||||
|
||||
|
||||
class Indexer:
|
||||
"""Coordinates the scan → chunk → embed → store pipeline."""
|
||||
|
||||
@@ -31,6 +37,7 @@ class Indexer:
|
||||
self.config = config
|
||||
self.vault_path = config_mod.resolve_vault_path(config)
|
||||
self._embedder = None # lazy init
|
||||
self._audit_logger = None # lazy init
|
||||
|
||||
@property
|
||||
def embedder(self):
|
||||
@@ -39,6 +46,38 @@ class Indexer:
|
||||
self._embedder = OllamaEmbedder(self.config)
|
||||
return self._embedder
|
||||
|
||||
@property
|
||||
def audit_logger(self):
|
||||
if self._audit_logger is None:
|
||||
from obsidian_rag.audit_logger import AuditLogger
|
||||
log_dir = _resolve_data_dir() / "audit"
|
||||
self._audit_logger = AuditLogger(log_dir / "audit.log")
|
||||
return self._audit_logger
|
||||
|
||||
def _check_sensitive_content_approval(self, chunks: list[dict[str, Any]]) -> None:
|
||||
"""Enforce user approval for sensitive content before indexing."""
|
||||
from obsidian_rag import security
|
||||
|
||||
sensitive_categories = self.config.security.require_confirmation_for
|
||||
if not sensitive_categories:
|
||||
return
|
||||
|
||||
for chunk in chunks:
|
||||
sensitivity = security.detect_sensitive(
|
||||
chunk['chunk_text'],
|
||||
self.config.security.sensitive_sections,
|
||||
self.config.memory.patterns
|
||||
)
|
||||
|
||||
for category in sensitive_categories:
|
||||
if sensitivity.get(category, False):
|
||||
if not self.config.security.auto_approve_sensitive:
|
||||
raise SensitiveContentError(
|
||||
f"Sensitive {category} content detected. "
|
||||
f"Requires explicit approval before indexing. "
|
||||
f"File: {chunk['source_file']}"
|
||||
)
|
||||
|
||||
def scan_vault(self) -> Generator[Path, None, None]:
|
||||
"""Walk vault, yielding markdown files to index."""
|
||||
for root, dirs, files in os.walk(self.vault_path):
|
||||
@@ -106,6 +145,26 @@ class Indexer:
|
||||
for idx, filepath in enumerate(files):
|
||||
try:
|
||||
num_chunks, enriched = self.process_file(filepath)
|
||||
# Enforce sensitive content policies
|
||||
self._check_sensitive_content_approval(enriched)
|
||||
|
||||
# Log sensitive content access
|
||||
for chunk in enriched:
|
||||
from obsidian_rag import security
|
||||
sensitivity = security.detect_sensitive(
|
||||
chunk['chunk_text'],
|
||||
self.config.security.sensitive_sections,
|
||||
self.config.memory.patterns
|
||||
)
|
||||
for category in ['health', 'financial', 'relations']:
|
||||
if sensitivity.get(category, False):
|
||||
self.audit_logger.log_sensitive_access(
|
||||
str(chunk['source_file']),
|
||||
category,
|
||||
'index',
|
||||
{'chunk_id': chunk['chunk_id']}
|
||||
)
|
||||
|
||||
# Embed chunks
|
||||
texts = [e["chunk_text"] for e in enriched]
|
||||
try:
|
||||
@@ -132,14 +191,13 @@ class Indexer:
|
||||
"total": total_files,
|
||||
}
|
||||
|
||||
return {
|
||||
# Yield final result
|
||||
yield {
|
||||
"indexed_files": indexed_files,
|
||||
"total_chunks": total_chunks,
|
||||
"duration_ms": 0, # caller can fill
|
||||
"errors": errors,
|
||||
}
|
||||
|
||||
def sync(self, on_progress: Iterator[dict] | None = None) -> dict[str, Any]:
|
||||
"""Incremental sync: only process files modified since last sync."""
|
||||
sync_result_path = self._sync_result_path()
|
||||
last_sync = None
|
||||
@@ -221,3 +279,78 @@ class Indexer:
|
||||
tmp = path.with_suffix(".json.tmp")
|
||||
tmp.write_text(json.dumps(result, indent=2))
|
||||
tmp.rename(path)
|
||||
|
||||
def sync(self, on_progress: Iterator[dict] | None = None) -> dict[str, Any]:
|
||||
"""Incremental sync: only process files modified since last sync."""
|
||||
sync_result_path = self._sync_result_path()
|
||||
last_sync = None
|
||||
if sync_result_path.exists():
|
||||
try:
|
||||
last_sync = json.loads(sync_result_path.read_text()).get("timestamp")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
db = get_db(self.config)
|
||||
table = create_table_if_not_exists(db)
|
||||
embedder = self.embedder
|
||||
|
||||
files = list(self.scan_vault())
|
||||
indexed_files = 0
|
||||
total_chunks = 0
|
||||
errors: list[dict] = []
|
||||
|
||||
for filepath in files:
|
||||
mtime = datetime.fromtimestamp(filepath.stat().st_mtime, tz=timezone.utc)
|
||||
mtime_str = mtime.isoformat()
|
||||
if last_sync and mtime_str <= last_sync:
|
||||
continue # unchanged
|
||||
|
||||
try:
|
||||
num_chunks, enriched = self.process_file(filepath)
|
||||
# Enforce sensitive content policies
|
||||
self._check_sensitive_content_approval(enriched)
|
||||
|
||||
# Log sensitive content access
|
||||
for chunk in enriched:
|
||||
from obsidian_rag import security
|
||||
sensitivity = security.detect_sensitive(
|
||||
chunk['chunk_text'],
|
||||
self.config.security.sensitive_sections,
|
||||
self.config.memory.patterns
|
||||
)
|
||||
for category in ['health', 'financial', 'relations']:
|
||||
if sensitivity.get(category, False):
|
||||
self.audit_logger.log_sensitive_access(
|
||||
str(chunk['source_file']),
|
||||
category,
|
||||
'index',
|
||||
{'chunk_id': chunk['chunk_id']}
|
||||
)
|
||||
|
||||
# Embed chunks
|
||||
texts = [e["chunk_text"] for e in enriched]
|
||||
try:
|
||||
vectors = embedder.embed_chunks(texts)
|
||||
except OllamaUnavailableError:
|
||||
vectors = [[0.0] * 1024 for _ in texts]
|
||||
for e, v in zip(enriched, vectors):
|
||||
e["vector"] = v
|
||||
upsert_chunks(table, enriched)
|
||||
total_chunks += num_chunks
|
||||
indexed_files += 1
|
||||
except Exception as exc:
|
||||
errors.append({"file": str(filepath), "error": str(exc)})
|
||||
|
||||
self._write_sync_result(indexed_files, total_chunks, errors)
|
||||
return {
|
||||
"indexed_files": indexed_files,
|
||||
"total_chunks": total_chunks,
|
||||
"errors": errors,
|
||||
}
|
||||
# Use the same dev-data-dir convention as config.py
|
||||
project_root = Path(__file__).parent.parent.parent
|
||||
data_dir = project_root / "obsidian-rag"
|
||||
if not data_dir.exists() and not (project_root / "KnowledgeVault").exists():
|
||||
data_dir = Path(os.path.expanduser("~/.obsidian-rag"))
|
||||
return data_dir / "sync-result.json"
|
||||
|
||||
|
||||
@@ -64,6 +64,19 @@ HTML_TAG_RE = re.compile(r"<[^>]+>")
|
||||
CODE_BLOCK_RE = re.compile(r"```[\s\S]*?```", re.MULTILINE)
|
||||
MULTI_WHITESPACE_RE = re.compile(r"\s+")
|
||||
MAX_CHUNK_LEN = 2000
|
||||
INJECTION_PATTERNS = [
|
||||
r"\x00", # Null bytes
|
||||
r"\x1a", # EOF character
|
||||
r"--\s", # SQL comment
|
||||
r"/\*[\s\S]*?\*/", # SQL comment
|
||||
r"';", # SQL injection
|
||||
r"\b(DROP|DELETE|INSERT|UPDATE|SELECT)\b", # SQL keywords
|
||||
r"<script[^>]*>.*?</script>", # XSS
|
||||
r"javascript:", # JS injection
|
||||
r"\b(eval|exec|spawn|fork|system)\b", # Code execution
|
||||
]
|
||||
|
||||
MAX_QUERY_LENGTH = 1000
|
||||
|
||||
|
||||
def sanitize_text(raw: str) -> str:
|
||||
@@ -86,6 +99,25 @@ def sanitize_text(raw: str) -> str:
|
||||
if len(text) > MAX_CHUNK_LEN:
|
||||
text = text[:MAX_CHUNK_LEN]
|
||||
return text
|
||||
"""Sanitize raw vault content before embedding.
|
||||
|
||||
- Strip HTML tags (prevent XSS)
|
||||
- Remove fenced code blocks
|
||||
- Normalize whitespace
|
||||
- Cap length at MAX_CHUNK_LEN chars
|
||||
"""
|
||||
# Remove fenced code blocks
|
||||
text = CODE_BLOCK_RE.sub(" ", raw)
|
||||
# Strip HTML tags
|
||||
text = HTML_TAG_RE.sub("", text)
|
||||
# Remove leading/trailing whitespace
|
||||
text = text.strip()
|
||||
# Normalize internal whitespace
|
||||
text = MULTI_WHITESPACE_RE.sub(" ", text)
|
||||
# Cap length
|
||||
if len(text) > MAX_CHUNK_LEN:
|
||||
text = text[:MAX_CHUNK_LEN]
|
||||
return text
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
@@ -93,6 +125,26 @@ def sanitize_text(raw: str) -> str:
|
||||
# ----------------------------------------------------------------------
|
||||
|
||||
|
||||
def sanitize_query(query: str) -> str:
|
||||
"""Sanitize search query to prevent prompt injection.
|
||||
|
||||
- Remove injection patterns
|
||||
- Normalize whitespace
|
||||
- Limit length
|
||||
"""
|
||||
# Remove injection patterns
|
||||
for pattern in INJECTION_PATTERNS:
|
||||
query = re.sub(pattern, " ", query, flags=re.IGNORECASE)
|
||||
|
||||
# Normalize whitespace
|
||||
query = MULTI_WHITESPACE_RE.sub(" ", query.strip())
|
||||
|
||||
# Limit length
|
||||
if len(query) > MAX_QUERY_LENGTH:
|
||||
query = query[:MAX_QUERY_LENGTH]
|
||||
|
||||
return query
|
||||
|
||||
def detect_sensitive(
|
||||
text: str,
|
||||
sensitive_sections: list[str],
|
||||
|
||||
149
python/tests/unit/test_security_fixes.py
Normal file
149
python/tests/unit/test_security_fixes.py
Normal file
@@ -0,0 +1,149 @@
|
||||
"""Tests for security fixes."""
|
||||
|
||||
import pytest
|
||||
from pathlib import Path
|
||||
from obsidian_rag.config import ObsidianRagConfig, SecurityConfig
|
||||
from obsidian_rag.embedder import OllamaEmbedder, SecurityError
|
||||
from obsidian_rag.indexer import Indexer, SensitiveContentError
|
||||
from obsidian_rag.security import sanitize_query
|
||||
|
||||
|
||||
def test_network_isolation_validation():
|
||||
"""Test that remote URLs are rejected when local_only=True."""
|
||||
config = ObsidianRagConfig(
|
||||
vault_path="/tmp/test",
|
||||
security=SecurityConfig(local_only=True)
|
||||
)
|
||||
|
||||
# Should allow localhost
|
||||
config.embedding.base_url = "http://localhost:11434"
|
||||
try:
|
||||
embedder = OllamaEmbedder(config)
|
||||
embedder._validate_network_isolation()
|
||||
except SecurityError:
|
||||
pytest.fail("Localhost should be allowed when local_only=True")
|
||||
|
||||
# Should reject remote URLs
|
||||
config.embedding.base_url = "http://example.com:11434"
|
||||
with pytest.raises(SecurityError, match="Remote embedding service not allowed"):
|
||||
embedder = OllamaEmbedder(config)
|
||||
embedder._validate_network_isolation()
|
||||
|
||||
# Should allow remote URLs when local_only=False
|
||||
config.security.local_only = False
|
||||
config.embedding.base_url = "http://example.com:11434"
|
||||
try:
|
||||
embedder = OllamaEmbedder(config)
|
||||
embedder._validate_network_isolation()
|
||||
except SecurityError:
|
||||
pytest.fail("Remote URLs should be allowed when local_only=False")
|
||||
|
||||
|
||||
def test_sensitive_content_enforcement():
|
||||
"""Test that sensitive content requires approval."""
|
||||
from obsidian_rag.config import MemoryConfig
|
||||
|
||||
config = ObsidianRagConfig(
|
||||
vault_path="/tmp/test",
|
||||
security=SecurityConfig(
|
||||
require_confirmation_for=["health"],
|
||||
auto_approve_sensitive=False,
|
||||
sensitive_sections=["#mentalhealth", "#physicalhealth", "#Relations"]
|
||||
),
|
||||
memory=MemoryConfig(
|
||||
patterns={
|
||||
"financial": ["owe", "owed", "debt", "paid", "$", "spent", "spend"],
|
||||
"health": ["#mentalhealth", "#physicalhealth", "medication", "therapy"],
|
||||
"commitments": ["shopping list", "costco", "amazon", "grocery"],
|
||||
}
|
||||
)
|
||||
)
|
||||
|
||||
indexer = Indexer(config)
|
||||
|
||||
# Create test chunks with health content
|
||||
chunks = [
|
||||
{
|
||||
'chunk_id': '1',
|
||||
'chunk_text': 'I have #mentalhealth issues and need therapy',
|
||||
'source_file': '/tmp/test/file.md',
|
||||
'source_directory': '/tmp/test',
|
||||
'section': 'content',
|
||||
'date': '2024-01-01',
|
||||
'tags': ['mentalhealth'],
|
||||
'chunk_index': 0,
|
||||
'total_chunks': 1,
|
||||
'modified_at': '2024-01-01T00:00:00Z',
|
||||
'indexed_at': '2024-01-01T00:00:00Z',
|
||||
}
|
||||
]
|
||||
|
||||
# Should raise SensitiveContentError
|
||||
with pytest.raises(SensitiveContentError, match="Sensitive health content detected"):
|
||||
indexer._check_sensitive_content_approval(chunks)
|
||||
|
||||
# Should pass when auto_approve_sensitive=True
|
||||
config.security.auto_approve_sensitive = True
|
||||
indexer = Indexer(config)
|
||||
try:
|
||||
indexer._check_sensitive_content_approval(chunks)
|
||||
except SensitiveContentError:
|
||||
pytest.fail("Should not raise when auto_approve_sensitive=True")
|
||||
|
||||
|
||||
def test_query_sanitization():
|
||||
"""Test that queries are properly sanitized."""
|
||||
# Test injection patterns
|
||||
dirty_query = "test'; DROP TABLE users; --"
|
||||
clean_query = sanitize_query(dirty_query)
|
||||
# The regex should remove the SQL injection pattern
|
||||
assert "'" not in clean_query or ";" not in clean_query
|
||||
|
||||
# Test that SQL keywords are removed
|
||||
sql_query = "SELECT * FROM users WHERE id = 1"
|
||||
clean_sql = sanitize_query(sql_query)
|
||||
assert "SELECT" not in clean_sql
|
||||
|
||||
# Test length limiting
|
||||
long_query = "a" * 2000
|
||||
short_query = sanitize_query(long_query)
|
||||
assert len(short_query) <= 1000
|
||||
|
||||
# Test whitespace normalization
|
||||
messy_query = " test \n query \t"
|
||||
clean_query = sanitize_query(messy_query)
|
||||
assert clean_query == "test query"
|
||||
|
||||
|
||||
def test_audit_logging():
|
||||
"""Test that audit logging works correctly."""
|
||||
from obsidian_rag.audit_logger import AuditLogger
|
||||
import tempfile
|
||||
import json
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
log_path = Path(tmpdir) / "audit.log"
|
||||
logger = AuditLogger(log_path)
|
||||
|
||||
# Log sensitive access
|
||||
logger.log_sensitive_access(
|
||||
"/tmp/test/health.md",
|
||||
"health",
|
||||
"index",
|
||||
{"chunk_id": "123"}
|
||||
)
|
||||
|
||||
# Verify log was created
|
||||
assert log_path.exists()
|
||||
|
||||
# Verify log content
|
||||
logs = json.loads(log_path.read_text())
|
||||
assert len(logs) == 1
|
||||
entry = logs[0]
|
||||
assert entry['content_type'] == 'health'
|
||||
assert entry['action'] == 'index'
|
||||
assert entry['metadata']['chunk_id'] == '123'
|
||||
|
||||
# Verify permissions
|
||||
import stat
|
||||
assert stat.S_IMODE(log_path.stat().st_mode) == 0o600
|
||||
Reference in New Issue
Block a user