Threat Model

Executive Summary

This document provides a comprehensive threat model for the Review Bot Automator project. It identifies assets, threat actors, attack vectors, and specific threat scenarios with risk ratings and mitigations based on the STRIDE methodology (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege).

Purpose: Enable security teams, auditors, and maintainers to understand the security landscape and evaluate risk posture.

Last Updated: 2025-11-25 Next Review: Quarterly or after major architectural changes

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Asset Identification

Critical Assets

1. Source Code Files

Description: Local source code files that the system reads and modifies.

Value: HIGH Justification: Contains intellectual property, business logic, and potentially sensitive data.

Protection Mechanisms:

• Path traversal prevention (InputValidator.validate_file_path())

• Atomic file operations (SecureFileHandler)

• Backup and rollback capabilities

• Secret scanning before modifications (SecretScanner)

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2. Git Repositories

Description: Version control system containing project history and code.

Value: HIGH Justification: Maintains integrity of code history, enables collaboration, critical for audit trails.

Protection Mechanisms:

• Read-only operations by default

• Commit signing support

• Git hook validation (future)

• Branch integrity verification

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3. GitHub API Tokens

Description: Authentication tokens for accessing GitHub API and repositories.

Value: CRITICAL Justification: Provides access to private repositories, can be used for unauthorized actions.

Protection Mechanisms:

• Token validation (InputValidator.validate_github_token())

• Secure token storage (environment variables, not in code)

• Secret scanning to prevent accidental exposure

• Token-based authentication with minimum required scopes

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4. User Data and PII

Description: Minimal user data collected (GitHub usernames, email addresses from commits).

Value: MEDIUM Justification: Subject to GDPR and privacy regulations, but limited collection.

Protection Mechanisms:

• Data minimization (collect only what’s necessary)

• No persistent storage of personal data

• Secure logging (no PII in logs)

• User consent for data processing

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5. File System Access

Description: Local file system where repositories are stored and modified.

Value: HIGH Justification: Compromise could lead to data loss, malware installation, or system access.

Protection Mechanisms:

• Workspace containment (resolve_file_path() with enforce_containment=True)

• Symlink prevention

• Permission checks before file operations

• Restricted file system scope

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6. CI/CD Pipeline

Description: GitHub Actions workflows that run security scans, tests, and fuzzing.

Value: HIGH Justification: Compromise could inject malicious code, bypass security controls, or expose secrets.

Protection Mechanisms:

• Pinned action versions (commit SHA)

• Step Security Harden Runner

• Restricted workflow permissions

• Secret scanning in workflows

• CodeQL analysis for workflow vulnerabilities

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Threat Actors

1. Malicious External Users

Capability: LOW to MEDIUM Motivation: Exploit vulnerabilities for data theft, system compromise, or reputation damage.

Attack Vectors:

• Malicious code suggestions via compromised CodeRabbit API

• Social engineering to trick users into applying malicious changes

• Exploiting publicly disclosed vulnerabilities

Typical Attacks: Path traversal, code injection, secret leakage

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2. Compromised Dependencies

Capability: MEDIUM to HIGH Motivation: Supply chain attack to inject malware, steal credentials, or backdoor systems.

Attack Vectors:

• Typosquatting on PyPI

• Compromised legitimate packages

• Dependency confusion attacks

Typical Attacks: Remote code execution, data exfiltration, persistent backdoors

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3. Insider Threats (Low Trust)

Capability: MEDIUM Motivation: Malicious insiders with access to codebase or CI/CD.

Attack Vectors:

• Direct code commits bypassing security reviews

• Modification of security configurations

• Disabling security controls

Typical Attacks: Logic bombs, backdoors, data theft

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4. Automated Attack Tools

Capability: LOW Motivation: Automated scanning for known vulnerabilities.

Attack Vectors:

• Vulnerability scanners

• Exploit frameworks (Metasploit, etc.)

• Botnet attacks

Typical Attacks: Known CVE exploitation, brute force, DoS

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STRIDE Threat Analysis

Spoofing (Identity Forgery)

T1: GitHub API Spoofing

Description: Attacker impersonates GitHub API to provide malicious code suggestions.

Impact: HIGH Likelihood: MEDIUM Risk Rating: HIGH

Attack Scenario:

1. Attacker performs MITM attack on network

2. Intercepts GitHub API calls

3. Provides malicious responses with crafted code suggestions

4. System applies malicious suggestions

Mitigations:

• ✅ HTTPS enforcement for all API calls (security.yml:348-350)

• ✅ Certificate validation (InputValidator.validate_github_url())

• ⏳ Certificate pinning (planned)

• ✅ Token-based authentication

Residual Risk: LOW (with HTTPS and token auth)

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T2: Git Commit Spoofing

Description: Attacker creates commits with forged author information.

Impact: MEDIUM Likelihood: MEDIUM Risk Rating: MEDIUM

Attack Scenario:

1. Attacker modifies git config

2. Sets fake author identity

3. Creates malicious commits with trusted identity

4. Commits appear to come from legitimate developers

Mitigations:

• ⏳ Git commit signing support (planned Phase 0.8)

• ✅ Audit logging of all operations

• ✅ Read-only git operations by default

Residual Risk: MEDIUM (until commit signing implemented)

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Tampering (Data Modification)

T3: Path Traversal Attack

Description: Attacker crafts file paths to access/modify files outside repository.

Impact: CRITICAL Likelihood: HIGH Risk Rating: CRITICAL

Attack Scenario:

1. Attacker provides suggestion with path: ../../etc/passwd

2. System resolves path outside workspace

3. Attacker reads sensitive system files

4. Potential overwrite of critical files

Mitigations:

• ✅ IMPLEMENTED: InputValidator.validate_file_path() (input_validator.py:131-230)

• ✅ Path normalization and resolution

• ✅ Workspace containment enforcement (enforce_containment=True)

• ✅ Symlink detection and rejection

• ✅ Relative path validation

Implementation Reference:

# json_handler.py:92-109
if not InputValidator.validate_file_path(
    path, allow_absolute=True, base_dir=str(self.workspace_root)
):
    self.logger.error(f"Invalid file path rejected: {path}")
    return False

file_path = resolve_file_path(
    path, self.workspace_root,
    allow_absolute=True, validate_workspace=True,
    enforce_containment=True
)

Residual Risk: VERY LOW (multiple layers of protection)

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T4: Code Injection via YAML/JSON/TOML

Description: Attacker injects executable code through configuration files.

Impact: CRITICAL Likelihood: MEDIUM Risk Rating: HIGH

Attack Scenario:

1. Attacker crafts malicious YAML:

   key: !!python/object/apply:os.system ["rm -rf /"]
   

2. System parses YAML with unsafe parser

3. Code executes during parsing

4. System compromise

Mitigations:

• ✅ IMPLEMENTED: Safe YAML parser (yaml.safe_load()) in input_validator.py:332-362

• ✅ Safe JSON parser with duplicate key detection (json_handler.py:442-465)

• ✅ Safe TOML parser (toml_handler.py)

• ✅ Whitelist of allowed data types

• ✅ No dynamic code execution

Implementation Reference:

# input_validator.py:348-362
try:
    yaml_data = yaml.safe_load(content)  # safe_load prevents !!python/
    if not isinstance(yaml_data, dict):
        return False, "YAML must be a dictionary at top level"
    return True, "Valid YAML"
except yaml.YAMLError as e:
    return False, f"Invalid YAML: {e}"

Residual Risk: VERY LOW (safe parsers enforced)

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T5: File System Race Conditions (TOCTOU)

Description: Time-of-check to time-of-use vulnerabilities in file operations.

Impact: MEDIUM Likelihood: LOW Risk Rating: LOW

Attack Scenario:

1. System checks file permissions

2. Attacker replaces file with malicious version

3. System operates on malicious file

4. Data corruption or unauthorized access

Mitigations:

• ✅ IMPLEMENTED: Atomic file operations (secure_file_handler.py:96-215)

• ✅ Temporary file with atomic rename (os.replace)

• ✅ File locking where applicable

• ✅ Transaction-like semantics

Implementation Reference:

# json_handler.py:169-188
with tempfile.NamedTemporaryFile(..., delete=False) as temp_file:
    temp_path = Path(temp_file.name)
    temp_file.write(json.dumps(merged_data, indent=2) + "\n")
    temp_file.flush()
    os.fsync(temp_file.fileno())  # Ensure written to disk

os.replace(temp_path, file_path)  # Atomic operation

Residual Risk: VERY LOW (atomic operations enforced)

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Repudiation (Denying Actions)

T6: Audit Log Tampering

Description: Attacker modifies or deletes logs to hide malicious activity.

Impact: MEDIUM Likelihood: LOW Risk Rating: LOW

Attack Scenario:

1. Attacker gains access to log files

2. Deletes or modifies incriminating log entries

3. Malicious activity goes undetected

4. Forensic investigation hampered

Mitigations:

• ✅ Secure logging (no secrets in logs)

• ✅ Structured logging with timestamps

• ⏳ Centralized log aggregation (future)

• ⏳ Immutable log storage (future)

Residual Risk: MEDIUM (until centralized logging)

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Information Disclosure (Data Leakage)

T7: Secret Leakage in Code Suggestions

Description: Attacker tricks system into applying suggestions containing secrets.

Impact: HIGH Likelihood: MEDIUM Risk Rating: HIGH

Attack Scenario:

1. Attacker crafts suggestion with embedded API key

2. System applies suggestion without detection

3. Secret committed to repository

4. Secret exposed in public repository

Mitigations:

• ✅ IMPLEMENTED: SecretScanner with 17 pattern types (secret_scanner.py:73-140)

• ✅ Pre-application secret scanning

• ✅ False positive filtering

• ✅ TruffleHog scanning in CI/CD

• ✅ GitGuardian integration (future)

Implementation Reference:

# secret_scanner.py:154-194
def scan_content(content: str, stop_on_first: bool = False) -> list[SecretFinding]:
    findings: list[SecretFinding] = []
    for finding in SecretScanner.scan_content_generator(content):
        findings.append(finding)
        if stop_on_first:
            break  # Early exit on first secret
    return findings

Patterns Detected:

• GitHub personal/OAuth/server/refresh tokens

• AWS access keys and secret keys

• OpenAI API keys

• JWT tokens

• Private keys (RSA, SSH, etc.)

• Slack tokens

• Google OAuth

• Azure connection strings

• Database URLs with passwords

• Generic API keys, passwords, secrets, tokens

Residual Risk: LOW (comprehensive scanning)

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T8: Sensitive Data in Error Messages

Description: Error messages leak sensitive file paths, content, or system info.

Impact: LOW Likelihood: MEDIUM Risk Rating: LOW

Attack Scenario:

1. Attacker triggers error conditions

2. Error messages reveal internal paths

3. Attacker maps file system structure

4. Information used for further attacks

Mitigations:

• ✅ Sanitized error messages (no stack traces in production)

• ✅ No file content in error output

• ✅ Generic error messages for users

• ✅ Detailed errors only in debug logs

Residual Risk: VERY LOW (sanitized errors)

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Denial of Service (Availability)

T9: Large File Processing DoS

Description: Attacker provides extremely large files to exhaust system resources.

Impact: MEDIUM Likelihood: MEDIUM Risk Rating: MEDIUM

Attack Scenario:

1. Attacker submits suggestion for 1GB file

2. System attempts to load entire file into memory

3. Out-of-memory condition

4. System crash or hang

Mitigations:

• ✅ File size limits (configurable)

• ✅ Memory-efficient streaming for large files (where applicable)

• ✅ Timeout mechanisms

• ⏳ Rate limiting (future)

Residual Risk: MEDIUM (file size limits configurable)

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T10: Algorithmic Complexity Attacks

Description: Attacker exploits worst-case performance of algorithms.

Impact: LOW Likelihood: LOW Risk Rating: LOW

Attack Scenario:

1. Attacker crafts pathological input

2. System uses O(n²) or worse algorithm

3. CPU exhaustion

4. Service degradation

Mitigations:

• ✅ Efficient algorithms (e.g., line-sweep for overlap calculation)

• ✅ ClusterFuzzLite fuzzing for performance regression detection

• ✅ Timeout mechanisms

Residual Risk: VERY LOW (efficient algorithms, fuzzing)

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Elevation of Privilege (Unauthorized Access)

T11: Privilege Escalation via File Permissions

Description: Attacker exploits improper file permissions to gain elevated access.

Impact: HIGH Likelihood: LOW Risk Rating: MEDIUM

Attack Scenario:

1. Attacker provides suggestion modifying file permissions

2. System applies suggestion without validation

3. Critical files made world-writable

4. Attacker gains unauthorized access

Mitigations:

• ✅ File permission preservation (json_handler.py:164-166, 183-185)

• ✅ Permission checks before operations

• ✅ No arbitrary file permission modifications

• ✅ Restricted file system scope

Implementation Reference:

# json_handler.py:164-166
if file_path.exists():
    original_mode = os.stat(file_path).st_mode

# ...after writing..
# json_handler.py:183-185
if original_mode is not None:
    os.chmod(temp_path, stat.S_IMODE(original_mode))

Residual Risk: LOW (permission preservation)

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T12: Dependency Confusion Attack

Description: Attacker publishes malicious package with same name to public repository.

Impact: HIGH Likelihood: LOW Risk Rating: MEDIUM

Attack Scenario:

1. Attacker identifies internal package name

2. Publishes malicious version to PyPI

3. Build system installs malicious package

4. Code execution and compromise

Mitigations:

• ✅ Dependency pinning (requirements-dev.txt with hashes)

• ✅ pip-compile --generate-hashes for integrity verification

• ✅ Dependency scanning (pip-audit + Trivy SBOM scanning)

• ✅ OpenSSF Scorecard monitoring for dependency hygiene

• ✅ Automatic Dependency Submission workflow

Residual Risk: LOW (multiple layers of dependency protection)

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LLM-Specific Threats (Phase 5)

T13: LLM Data Exfiltration via PR Comments

Description: Sensitive data (secrets, credentials) in PR comments sent to external LLM APIs.

Impact: HIGH Likelihood: MEDIUM Risk Rating: HIGH

Attack Scenario:

1. User posts PR comment containing API keys or credentials

2. Comment body is processed by LLM parser

3. Secrets are sent to external LLM API (Anthropic/OpenAI)

4. Credentials exposed to third-party service

Mitigations:

• ✅ IMPLEMENTED: SecretScanner.scan_content() before LLM calls (parser.py:147-158)

• ✅ IMPLEMENTED: LLMSecretDetectedError raised when secrets detected

• ✅ 17 secret detection patterns covering major providers

• ✅ Configurable scan_for_secrets parameter (default: True)

Residual Risk: LOW (comprehensive pre-LLM secret scanning)

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T14: Prompt Injection Attack

Description: Malicious PR comments containing prompts designed to manipulate LLM responses.

Impact: MEDIUM Likelihood: MEDIUM Risk Rating: MEDIUM

Attack Scenario:

1. Attacker crafts PR comment with embedded instructions

2. Comment processed by LLM parser

3. LLM follows injected instructions instead of parsing intent

4. Malicious code suggestions generated

Mitigations:

• ✅ Structured JSON output format enforced

• ✅ Schema validation on all ParsedChange objects

• ✅ Confidence threshold filtering (default: 0.5)

• ✅ Invalid JSON responses rejected

Residual Risk: MEDIUM (inherent LLM limitation, multiple validation layers)

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T15: LLM Cache Poisoning

Description: Attacker attempts to poison prompt cache with malicious responses.

Impact: MEDIUM Likelihood: LOW Risk Rating: LOW

Attack Scenario:

1. Attacker crafts comment that generates specific cache key

2. Malicious response cached

3. Future identical prompts return poisoned response

4. Malicious code suggestions served from cache

Mitigations:

• ✅ SHA-256 hash-based cache keys (collision-resistant)

• ✅ Cache stores prompt hash, not actual prompt text

• ✅ Cache files have 0600 permissions (owner-only)

• ✅ Cache directory has 0700 permissions

Residual Risk: VERY LOW (cryptographic hash prevents practical collision attacks)

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T16: LLM Cost Exhaustion Attack

Description: Attacker triggers excessive LLM API calls to exhaust budget or cause financial harm.

Impact: LOW Likelihood: LOW Risk Rating: LOW

Attack Scenario:

1. Attacker creates many PR comments

2. Each comment triggers LLM API call

3. Budget exhausted rapidly

4. Financial impact or denial of service

Mitigations:

• ✅ IMPLEMENTED: CostTracker with configurable budget

• ✅ IMPLEMENTED: LLMCostExceededError when budget exceeded

• ✅ Warning at configurable threshold (default: 80%)

• ✅ Graceful fallback to regex parsing

• ✅ Rate limiting in ParallelLLMParser

Residual Risk: LOW (budget enforcement with graceful degradation)

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T17: API Key Exposure in Error Messages

Description: API keys or secrets leaked in error messages or logs.

Impact: HIGH Likelihood: MEDIUM Risk Rating: MEDIUM

Attack Scenario:

1. LLM provider returns error containing request details

2. Error message includes API key or sensitive data

3. Error logged or displayed to user

4. Credentials exposed

Mitigations:

• ✅ IMPLEMENTED: ResilientLLMProvider sanitizes exception messages

• ✅ IMPLEMENTED: SecretScanner.has_secrets() checks error strings

• ✅ Secrets in errors replaced with “(details redacted)”

• ✅ API keys stored in environment variables, not code

Residual Risk: LOW (automatic sanitization of error messages)

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Risk Matrix

Threat ID	Threat	Impact	Likelihood	Risk	Status
T1	GitHub API Spoofing	HIGH	MEDIUM	HIGH	✅ Mitigated
T2	Git Commit Spoofing	MEDIUM	MEDIUM	MEDIUM	⏳ Partial
T3	Path Traversal Attack	CRITICAL	HIGH	CRITICAL	✅ Mitigated
T4	Code Injection (YAML/JSON/TOML)	CRITICAL	MEDIUM	HIGH	✅ Mitigated
T5	File System Race Conditions	MEDIUM	LOW	LOW	✅ Mitigated
T6	Audit Log Tampering	MEDIUM	LOW	LOW	⏳ Partial
T7	Secret Leakage	HIGH	MEDIUM	HIGH	✅ Mitigated
T8	Sensitive Data in Errors	LOW	MEDIUM	LOW	✅ Mitigated
T9	Large File DoS	MEDIUM	MEDIUM	MEDIUM	⏳ Partial
T10	Algorithmic Complexity	LOW	LOW	LOW	✅ Mitigated
T11	Privilege Escalation	HIGH	LOW	MEDIUM	✅ Mitigated
T12	Dependency Confusion	HIGH	LOW	MEDIUM	✅ Mitigated
T13	LLM Data Exfiltration	HIGH	MEDIUM	HIGH	✅ Mitigated
T14	Prompt Injection	MEDIUM	MEDIUM	MEDIUM	⏳ Partial
T15	LLM Cache Poisoning	MEDIUM	LOW	LOW	✅ Mitigated
T16	LLM Cost Exhaustion	LOW	LOW	LOW	✅ Mitigated
T17	API Key in Errors	HIGH	MEDIUM	MEDIUM	✅ Mitigated

Legend:

• ✅ Mitigated: Controls fully implemented

• ⏳ Partial: Controls partially implemented or planned

• ❌ Unmitigated: No controls in place

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Security Control Mapping

Control	Threats Addressed	Implementation	Effectiveness
InputValidator	T1, T3, T4, T7	input_validator.py	HIGH
SecretScanner	T7	secret_scanner.py	HIGH
SecureFileHandler	T3, T5, T11	secure_file_handler.py	HIGH
Safe Parsers	T4	yaml.safe_load, json.loads	HIGH
Atomic File Operations	T5	os.replace, tempfile	HIGH
Path Resolution	T3	path_utils.py	HIGH
Dependency Scanning	T12	pip-audit, Trivy, OpenSSF Scorecard	HIGH
Fuzzing	T9, T10	ClusterFuzzLite	MEDIUM
Secret Scanning (CI)	T7	TruffleHog, Scorecard	HIGH
HTTPS Enforcement	T1	GitHub API client	HIGH
LLM Pre-Scan	T13, T17	parser.py, SecretScanner	HIGH
CostTracker	T16	cost_tracker.py	HIGH
ResilientLLMProvider	T17	resilient_provider.py	HIGH
PromptCache	T15	cache/prompt_cache.py	HIGH
ParallelLLMParser	T16	parallel_parser.py	HIGH

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Recommendations

Immediate Actions (0-30 days)

1. Implement commit signing: Add GPG commit signing support (addresses T2)

2. Centralized logging: Implement immutable log aggregation (addresses T6)

3. Rate limiting: Add configurable rate limits for API calls and file operations (addresses T9)

Short-term (1-3 months)

1. Certificate pinning: Implement cert pinning for GitHub API (addresses T1)

2. Sandboxing: Explore containerized execution for additional isolation (addresses T4, T11)

3. Audit trail: Implement cryptographic audit trail for all operations (addresses T6)

Long-term (3-6 months)

1. Penetration testing: Regular third-party security audits

2. Bug bounty program: Public bug bounty to incentivize security research

3. Security monitoring: Real-time security event monitoring and alerting

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References

• STRIDE Methodology: https://learn.microsoft.com/en-us/security/compass/applications-services-threat-modeling

• OWASP Threat Modeling: https://owasp.org/www-community/Threat_Modeling

• CWE Top 25: https://cwe.mitre.org/top25/

• Security Architecture: docs/security-architecture.md

• Implementation: src/review_bot_automator/security/

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Document Version: 1.0 Last Updated: 2025-11-25 Next Review: 2026-02-03 (Quarterly) Owner: Security Team Approval: Pending