Table of Contents
The rapidly evolving landscape of cryptography is increasingly threatened by two disruptive forces: quantum attacks and side-channel attacks. Quantum computers, still in their early development, promise to break the core assumptions behind much of today’s cryptographic infrastructure. Meanwhile, side-channel attacks exploit physical properties of cryptographic devices—such as their power consumption—to extract secrets, sometimes bypassing even the strongest algorithms. Recently, researchers have explored the intersection of these threats, studying power-based side-channel attacks on quantum computers themselves [2].
In this comprehensive blog post, we’ll cover:
Cryptography, the science of securing communication, rests on mathematical assumptions—mainly that certain problems (like factoring large numbers or computing discrete logarithms) are infeasible to solve on classical computers. Most security systems use two classes of cryptography:
Techniques such as RSA and ECC are widespread but rely on problems quantum computers can solve efficiently. Furthermore, regardless of the perceived mathematical security, real devices can leak information through observable behaviors—enter side-channel attacks.
Quantum computing harnesses the power of quantum mechanics to process complex computations unfeasible for classical computers. This has profound implications for cryptographic systems.
Quantum computers can use Shor’s algorithm to factorize integers and compute discrete logarithms exponentially faster than classical methods. This breaks the security underlying popular public-key schemes:
Impact: Once practical quantum computers are available, all data protected by RSA and ECC are vulnerable to decryption—even data archived today (“store now, decrypt later” attack).
Example:
Symmetric systems like AES are more resistant but still affected. Grover’s algorithm provides a quadratic speed-up in brute-forcing keys:
Mitigation: Use longer keys for symmetric schemes in a post-quantum world (e.g., AES-256 instead of AES-128).
While cryptographic algorithms may be mathematically secure, their implementations can unintentionally leak information. Side-channel attacks (SCAs) exploit observable characteristics such as:
Attackers observe these physical or behavioral emissions during cryptographic operations to infer keys and secrets without breaking the math.
A 2023 study [2] presented the first exploration of power-based side-channel attacks in quantum computers. While quantum computing is lauded for new security approaches (e.g., quantum key distribution), real quantum processors—especially those with classical control electronics—can be vulnerable to classical side-channels.
Key highlights:
Modern hardware-based security, like Hardware Security Modules (HSMs) and hardware intellectual property (IP) crypto cores, faces increasing scrutiny for both quantum and side-channel vulnerability. Hardware is where theory meets practice, and subtle implementation flaws can mean catastrophic leaks.
Post-Quantum Cryptography refers to cryptographic algorithms based on mathematical problems believed to be robust even against quantum computers (e.g., lattice-based, hash-based, code-based).
Transition in hardware:
Ensuring that “quantum-safe” hardware implementations are also side-channel resilient is nontrivial. The hardware transitions give rise to a new set of attack vectors:
PQShield works with governments and major hardware vendors to embed PQC with side-channel countermeasures into hardware chips, used in applications ranging from military communication devices to consumer electronics.
A classic SPA attack traces simple RSA operations on a smartcard. By analyzing the power consumption at each squaring and multiplication, the attacker deduces the bits of the private key.
Quantum adversaries may combine Grover's algorithm (to reduce brute-force search time) with power side-channel analysis of classical or quantum-resistant hardware to further enhance attacks.
From [2]: By instrumenting the power rail to a quantum processor, researchers observed distinct signatures for single-qubit and multi-qubit operations, inferring some of the underlying algorithmic steps—a first demonstration that quantum systems also need traditional SCA defenses.
Research shows that early lattice-based PQC software and hardware can be vulnerable to power and timing analysis, resurrecting the need for long-studied countermeasures.
Even if you’re not manufacturing hardware, you can perform basic side-channel inspections on embedded devices and cryptographic chips using inexpensive laboratory setups. Below are beginner-friendly (yet practical) code examples for scanning interfaces and parsing output related to SCA/PQC hardware testing.
Suppose your lab has connected multiple cryptographic devices via USB, and you want to identify the relevant devices for side-channel analysis.
# List all connected USB devices (useful for identifying crypto hardware IPs)
lsusb
# Detailed view of a specific device (replace ID with your device)
lsusb -d 1a2b:3c4d -v
# Check all connected serial ports (common for smartcards/HSMs/sensors)
ls /dev/tty*
# Use udevadm to get device attributes for a connected hardware crypto module
udevadm info -a -n /dev/ttyUSB0
# If your power-analysis gear uses a specific interface, check dmesg output
dmesg | grep -i usb
Tip: For consistency and automation, combine these queries into scripts for batch processing.
Say you’ve collected thousands of power measurements in CSV format. Parsing and analyzing for possible correlations (evidence of SCA vulnerability) is straightforward in Python.
import numpy as np
import matplotlib.pyplot as plt
# Load power trace data from CSV (rows: time points, columns: traces for different operations)
data = np.loadtxt("power_traces.csv", delimiter=",")
# Plot the first 5 traces for visual inspection
for i in range(5):
plt.plot(data[:, i], label=f"Trace {i+1}")
plt.title("Sample Power Traces from Cryptographic Hardware")
plt.xlabel("Time (samples)")
plt.ylabel("Power (arbitrary units)")
plt.legend()
plt.show()
# Basic correlation analysis for potential leakage (across traces)
correlations = np.corrcoef(data)
print("Correlation matrix shape:", correlations.shape)
# Look for high off-diagonal values (unexpectedly correlated traces)
thresh = 0.85
leaky_pairs = np.argwhere((correlations > thresh) & (correlations < 0.999))
for x, y in leaky_pairs:
print(f"Possible leakage between trace {x} and {y} (corr={correlations[x, y]:.2f})")
Advanced: Extend this code for higher-level DPA/SCA techniques; integrate with open-source SCA toolkits like ChipWhisperer.
The impending quantum era necessitates a comprehensive approach to cryptographic agility and device resilience:
The twin threats of quantum and side-channel attacks underscore a sobering reality: No single defensive strategy suffices. Post-Quantum Cryptography offers new hope, but without thoughtful hardware implementation and ongoing side-channel analysis, secrets may still leak. As quantum hardware matures, it too will face side-channel scrutiny, blurring the lines between new and old attack techniques.
Enterprises, governments, and hardware manufacturers must adopt a layered, proactive defense—integrating quantum-resistant algorithms with robust side-channel protections—preserving security today and for the quantum-enabled future.
Keywords for SEO: quantum attacks, side-channel, post-quantum cryptography, hardware security IP, power analysis, PQC, crypto hardware, Bash script parsing, Python SCA analytics, real-world quantum security.
If you found this content valuable, imagine what you could achieve with our comprehensive 47-week elite training program. Join 1,200+ students who've transformed their careers with Unit 8200 techniques.