Constant Time Analysis

Constant Time Analysis is a community skill for verifying that cryptographic code executes in constant time, covering timing side-channel detection, branch analysis, memory access pattern review, compiler optimization checks, and automated verification for security-critical implementations.

What Is This?

Overview

Constant Time Analysis provides patterns for ensuring cryptographic implementations do not leak secret data through execution timing. It covers timing side-channel detection that identifies code paths where execution time varies based on secret inputs, branch analysis that flags conditional statements that depend on secret values, memory access pattern review that checks for secret-dependent array indexing, compiler optimization checks that verify the compiler has not introduced timing variations through optimization passes, and automated verification that runs analysis tools to confirm constant-time properties. The skill enables developers to write cryptographic code resistant to timing attacks, which are particularly dangerous because they require no special hardware access and can be executed remotely over a network connection.

Who Should Use This

This skill serves cryptography engineers implementing secure algorithms, security auditors reviewing cryptographic library code, and embedded developers writing timing-sensitive security functions. It is also relevant for developers integrating third-party cryptographic libraries who need to verify that those libraries meet constant-time requirements before deployment.

Why Use It?

Problems It Solves

Timing side channels leak secret key material through observable execution time differences. Conditional branches on secret data create timing variations attackers can measure. Cache-line dependent memory access patterns leak information about secret array indexes. Compiler optimizations can introduce timing variations even when source code appears constant-time.

Core Highlights

Branch detector identifies conditionals dependent on secret variables. Memory analyzer checks for secret-indexed array access patterns. Timing measurer profiles execution time across different input values. Compiler checker verifies that optimizations preserve constant-time properties.

How to Use It?

Basic Usage

import ast
from dataclasses\
  import dataclass
from pathlib import Path

@dataclass
class TimingIssue:
  file: str
  line: int
  issue_type: str
  description: str

class ConstantTimeChecker:
  def __init__(
    self,
    secret_vars:\
      list[str]
  ):
    self.secrets = set(
      secret_vars)
    self.issues:\
      list[TimingIssue] = []

  def check_file(
    self, path: str
  ) -> list[TimingIssue]:
    code = Path(path)\
      .read_text()
    tree = ast.parse(code)
    self.issues = []
    self._check_branches(
      tree, path)
    self._check_indexing(
      tree, path)
    return self.issues

  def _check_branches(
    self,
    tree: ast.AST,
    path: str
  ):
    for node in ast.walk(
        tree):
      if isinstance(
          node, ast.If):
        names = [
          n.id for n
          in ast.walk(
            node.test)
          if isinstance(
            n, ast.Name)]
        if any(n in
            self.secrets
            for n in names):
          self.issues.append(
            TimingIssue(
              path,
              node.lineno,
              'branch',
              'Secret-dependent'
              ' branch'))

Real-World Examples

def ct_compare(
  a: bytes,
  b: bytes
) -> bool:
  if len(a) != len(b):
    return False
  result = 0
  for x, y in zip(a, b):
    result |= x ^ y
  return result == 0

def ct_select(
  condition: int,
  a: int,
  b: int
) -> int:
  mask = -(condition & 1)
  return (a & mask) | (
    b & ~mask)

Advanced Tips

Use hardware performance counters to measure actual timing variance across different secret inputs. Run analysis on compiled assembly or binary output rather than source code alone since compilers may introduce branches. Apply dudect-style statistical testing with large sample sizes to detect small timing differences. When reviewing assembly output, pay particular attention to conditional move instructions, as some compilers fail to generate them consistently even when the source code uses branchless patterns.

When to Use It?

Use Cases

Verify that a password comparison function executes in constant time regardless of where characters differ. Audit an AES implementation for secret-dependent branches and memory access patterns. Check that a compiler has not introduced timing leaks in optimized cryptographic code.

Related Topics

Side-channel attacks, constant-time programming, cryptographic security, timing analysis, and secure coding.

Important Notes

Requirements

Source code access for static analysis of branch and memory patterns. Profiling tools for timing measurement across input values. Knowledge of which variables hold secret data for taint analysis.

Usage Recommendations

Do: analyze compiled output in addition to source code since compiler optimizations change control flow. Use bitwise operations instead of conditional branches for secret-dependent selections. Test with statistical timing analysis tools to verify constant-time behavior empirically.

Don't: assume source-level constant-time code remains constant-time after compilation without verification. Use early-return patterns in functions that process secret data. Rely solely on static analysis without timing measurement validation.

Limitations

Static analysis can detect branch patterns but cannot confirm actual timing behavior on specific hardware. Compiler updates may introduce new optimizations that break constant-time properties. Microarchitectural effects like speculative execution create timing channels not visible in code analysis. Different CPU architectures may have different timing behaviors for the same instruction sequence requiring platform-specific testing.