# Threat Model for clock-bigint

## Overview

clock-bigint is a cryptographic big integer library designed for blockchain consensus engines. This document outlines the threat model, security assumptions, and mitigation strategies.

## Security Objectives

1. **Confidentiality**: Prevent leakage of sensitive cryptographic material through side channels
2. **Integrity**: Ensure mathematical correctness of all operations
3. **Availability**: Maintain consistent performance regardless of input
4. **Constant-time execution**: Prevent timing-based side channel attacks

## Assets

- **Cryptographic keys**: Private keys used in consensus protocols
- **Intermediate values**: Temporary results during cryptographic computations
- **Performance characteristics**: Timing and power consumption patterns

## Attack Vectors

### 1. Side Channel Attacks

#### Timing Attacks
- **Threat**: Attackers can infer secret information by measuring operation timing
- **Impact**: Key recovery, signature forgery
- **Likelihood**: High (timing attacks are practical on most systems)
- **Mitigation**:
  - All operations execute in constant time
  - Branch-free algorithms
  - Fixed memory access patterns
  - No early returns based on secret data

#### Power Analysis Attacks
- **Threat**: Attackers can analyze power consumption patterns
- **Impact**: Key recovery through differential power analysis
- **Likelihood**: Medium (requires physical access or specialized equipment)
- **Mitigation**:
  - Constant-time operations reduce power variation
  - Montgomery arithmetic reduces operation-specific patterns

#### Cache Attacks
- **Threat**: Attackers can observe cache access patterns
- **Impact**: Information leakage through cache timing
- **Likelihood**: Medium-High (practical on shared systems)
- **Mitigation**:
  - Minimize cache-dependent operations
  - Use fixed access patterns
  - Avoid secret-dependent memory access

### 2. Mathematical Attacks

#### Incorrect Results
- **Threat**: Computation errors leading to invalid cryptographic results
- **Impact**: Consensus failures, invalid transactions
- **Likelihood**: Low (due to comprehensive testing)
- **Mitigation**:
  - Extensive test coverage
  - Property-based testing
  - Fuzzing for edge cases
  - Formal verification of algorithms

#### Implementation Vulnerabilities
- **Threat**: Bugs in arithmetic operations
- **Impact**: Cryptographic weakness, consensus compromise
- **Likelihood**: Medium
- **Mitigation**:
  - Comprehensive unit tests
  - Integration tests
  - Fuzz testing
  - Code review

### 3. Resource Exhaustion

#### Computational DoS
- **Threat**: Excessive resource usage in consensus-critical operations
- **Impact**: Network slowdown, consensus delays
- **Likelihood**: Medium
- **Mitigation**:
  - Gas metering for operations
  - Precomputable operation costs
  - Timeboxed operations for large computations

### 4. Supply Chain Attacks

#### Dependency Compromise
- **Threat**: Malicious dependencies or build system compromise
- **Impact**: Backdoored cryptography
- **Likelihood**: Low-Medium
- **Mitigation**:
  - Minimal dependencies
  - Audited dependency tree
  - Reproducible builds

## Security Assumptions

### Trust Assumptions
- **Hardware**: Trusted execution environment
- **Compiler**: Rust compiler correctly implements constant-time operations
- **Operating System**: Does not leak timing information
- **Users**: Follow security best practices

### Environmental Assumptions
- **Threat Model Scope**: Software-only attacks
- **Adversary Capabilities**:
  - Network access to observe timing
  - Local execution access
  - Ability to choose inputs to cryptographic operations
  - No physical access to hardware
  - No access to secret keys during computation

## Security Architecture

### Constant-Time Design
- All arithmetic operations are constant-time
- No secret-dependent branches or memory access
- Fixed execution paths for all inputs

### Memory Safety
- Rust's ownership system prevents memory corruption
- No unsafe code in cryptographic paths
- Bounds checking prevents buffer overflows

### Algorithm Selection
- Montgomery arithmetic for modular operations
- Karatsuba multiplication for large operands
- Comba method for small multiplications
- All algorithms chosen for constant-time properties

## Verification and Testing

### Automated Testing
- Unit tests for all operations
- Property-based testing with Proptest
- Fuzz testing with cargo-fuzz
- Constant-time verification tests

### Manual Verification
- Code review for constant-time properties
- Formal verification of critical algorithms
- Security audit by independent parties

### Continuous Monitoring
- Performance regression testing
- Fuzzing in CI/CD pipeline
- Dependency vulnerability scanning

## Incident Response

### Detection
- Automated monitoring for timing anomalies
- Performance benchmark comparisons
- Fuzz testing failure alerts

### Response
- Immediate cessation of affected operations
- Security advisory publication
- Patch development and deployment
- Post-mortem analysis

## Compliance and Standards

- **FIPS 140-3**: Cryptographic module requirements
- **NIST SP 800-57**: Key management
- **RFC 7748**: Elliptic curve cryptography
- **Constant-time cryptography**: No secret-dependent timing

## Risk Assessment

| Risk | Probability | Impact | Mitigation Status |
|------|-------------|--------|-------------------|
| Timing attacks | High | Critical | Implemented (constant-time) |
| Mathematical errors | Low | Critical | Implemented (testing, fuzzing) |
| Implementation bugs | Medium | High | Implemented (testing, review) |
| Supply chain attacks | Low | Critical | Partial (dependency audit) |
| Resource exhaustion | Medium | Medium | Implemented (gas metering) |

## Future Considerations

- **Quantum resistance**: Current algorithms remain secure
- **Post-quantum cryptography**: Library extensible for PQC algorithms
- **Hardware acceleration**: Consider CPU-specific optimizations
- **Formal verification**: Expand formal proofs of constant-time properties