Constant Time Analysis

Constant-Time Analysis

Analyze cryptographic code to detect operations that leak secret data through execution timing variations.

When to Use

User writing crypto code? ──yes──> Use this skill
         │
         no
         │
         v
User asking about timing attacks? ──yes──> Use this skill
         │
         no
         │
         v
Code handles secret keys/tokens? ──yes──> Use this skill
         │
         no
         │
         v
Skip this skill

Concrete triggers:

• User implements signature, encryption, or key derivation
• Code contains / or % operators on secret-derived values
• User mentions "constant-time", "timing attack", "side-channel", "KyberSlash"
• Reviewing functions named sign, verify, encrypt, decrypt, derive_key

When NOT to Use

• Non-cryptographic code (business logic, UI, etc.)
• Public data processing where timing leaks don't matter
• Code that doesn't handle secrets, keys, or authentication tokens
• High-level API usage where timing is handled by the library

Language Selection

Based on the file extension or language context, refer to the appropriate guide:

Quick Start

# Analyze any supported file type
uv run {baseDir}/ct_analyzer/analyzer.py <source_file>

# Include conditional branch warnings
uv run {baseDir}/ct_analyzer/analyzer.py --warnings <source_file>

# Filter to specific functions
uv run {baseDir}/ct_analyzer/analyzer.py --func 'sign|verify' <source_file>

# JSON output for CI
uv run {baseDir}/ct_analyzer/analyzer.py --json <source_file>

Native Compiled Languages Only (C, C++, Go, Rust)

# Cross-architecture testing (RECOMMENDED)
uv run {baseDir}/ct_analyzer/analyzer.py --arch x86_64 crypto.c
uv run {baseDir}/ct_analyzer/analyzer.py --arch arm64 crypto.c

# Multiple optimization levels
uv run {baseDir}/ct_analyzer/analyzer.py --opt-level O0 crypto.c
uv run {baseDir}/ct_analyzer/analyzer.py --opt-level O3 crypto.c

VM-Compiled Languages (Java, Kotlin, C#)

# Analyze Java bytecode
uv run {baseDir}/ct_analyzer/analyzer.py CryptoUtils.java

# Analyze Kotlin bytecode (Android/JVM)
uv run {baseDir}/ct_analyzer/analyzer.py CryptoUtils.kt

# Analyze C# IL
uv run {baseDir}/ct_analyzer/analyzer.py CryptoUtils.cs

Note: Java, Kotlin, and C# compile to bytecode (JVM/CIL) that runs on a virtual machine with JIT compilation. The analyzer examines the bytecode directly, not the JIT-compiled native code. The --arch and --opt-level flags do not apply to these languages.

Swift (iOS/macOS)

# Analyze Swift for native architecture
uv run {baseDir}/ct_analyzer/analyzer.py crypto.swift

# Analyze for specific architecture (iOS devices)
uv run {baseDir}/ct_analyzer/analyzer.py --arch arm64 crypto.swift

# Analyze with different optimization levels
uv run {baseDir}/ct_analyzer/analyzer.py --opt-level O0 crypto.swift

Note: Swift compiles to native code like C/C++/Go/Rust, so it uses assembly-level analysis and supports --arch and --opt-level flags.

Prerequisites

macOS users: Homebrew installs Java and .NET as "keg-only". You must add them to your PATH:

# For Java (add to ~/.zshrc)
export PATH="/opt/homebrew/opt/openjdk@21/bin:$PATH"

# For .NET tools (add to ~/.zshrc)
export PATH="$HOME/.dotnet/tools:$PATH"

See references/vm-compiled.md for detailed setup instructions and troubleshooting.

Quick Reference

Interpreting Results

PASSED - No variable-time operations detected.

FAILED - Dangerous instructions found. Example:

[ERROR] SDIV
  Function: decompose_vulnerable
  Reason: SDIV has early termination optimization; execution time depends on operand values

Verifying Results (Avoiding False Positives)

CRITICAL: Not every flagged operation is a vulnerability. The tool has no data flow analysis - it flags ALL potentially dangerous operations regardless of whether they involve secrets.

For each flagged violation, ask: Does this operation's input depend on secret data?

1. Identify the secret inputs to the function (private keys, plaintext, signatures, tokens)

2. Trace data flow from the flagged instruction back to inputs

3. Common false positive patterns:

   // FALSE POSITIVE: Division uses public constant, not secret
   int num_blocks = data_len / 16;  // data_len is length, not content
   
   // TRUE POSITIVE: Division involves secret-derived value
   int32_t q = secret_coef / GAMMA2;  // secret_coef from private key
   

4. Document your analysis for each flagged item

Quick Triage Questions

Limitations

1. Static Analysis Only: Analyzes assembly/bytecode, not runtime behavior. Cannot detect cache timing or microarchitectural side-channels.

2. No Data Flow Analysis: Flags all dangerous operations regardless of whether they process secrets. Manual review required.

3. Compiler/Runtime Variations: Different compilers, optimization levels, and runtime versions may produce different output.

Real-World Impact

• KyberSlash (2023): Division instructions in post-quantum ML-KEM implementations allowed key recovery
• Lucky Thirteen (2013): Timing differences in CBC padding validation enabled plaintext recovery
• RSA Timing Attacks: Early implementations leaked private key bits through division timing

References

• Cryptocoding Guidelines - Defensive coding for crypto
• KyberSlash - Division timing in post-quantum crypto
• BearSSL Constant-Time - Practical constant-time techniques

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior decisions and patterns to avoid re-discovering solutions. Cache results for instant retrieval in future sessions.

# Check for prior development context before starting
python3 execution/memory_manager.py auto --query "prior work and patterns related to Constant Time Analysis"

Storing Results

After completing work, store development decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Completed task with key insights documented for future reference" \
  --type decision --project <project> \
  --tags constant-time-analysis default

Multi-Agent Collaboration

Share outcomes with other agents so the team stays aligned and avoids duplicate work.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Task completed — results documented and shared with team" \
  --project <project>