Weak RNG in Embedded Devices

Updated 31 December 2025

Weak RNG in embedded devices is defined by limited physical entropy sources leading to bias, deterministic outputs, and vulnerability in cryptographic systems.
Empirical studies highlight that embedded systems, using sources like SRAM startup and oscillator jitter, often produce far less entropy per sample than needed for secure operations.
Adaptive techniques such as dynamic threshold digitizers, entropy pooling, and post-processing extractions are essential to mitigate these weaknesses and enhance RNG reliability.

Weak random number generation in embedded devices describes the persistent vulnerability where random number generators (RNGs) exhibit bias, low entropy, or deterministic behavior, critically weakening cryptographic systems or secure applications. Unlike server-class hardware, embedded systems often lack robust physical entropy sources and are highly sensitive to process, voltage, and temperature (PVT) variation, resource constraints, and attack surfaces targeting limited entropy or compromised randomness pathways.

1. Causes and Manifestations of Weak RNGs in Embedded Devices

Embedded RNGs are frequently limited by both architectural and physical causes:

Entropy Source Limitations: Many embedded systems operate with minimal hardware entropy, often relying on analog sensors (e.g., ADC readings, thermal or oscillator noise), SRAM startup state, or derived environmental readings. Empirical evidence shows these sources deliver low min-entropy per bit: for SRAM-PUFs, 4.2%–5.5% (Kietzmann et al., 2020); in uninitialized SRAM, ≈0.1 bits per bit (0907.1256).
Bias and Non-Robustness: True-random sources such as stochastic MTJs or volatile process-driven DRAM error mechanisms may have their output bias shift under PVT variation, as demonstrated for sMTJs by Zahoor et al., where bias in the 0/1 ratio tracks drift in conductance or voltage (Zahoor et al., 7 Jul 2025).
Resource Constraints: Ultra-low-cost MCUs and RFID tags suffer additional bottlenecks: minimal hardware RNG integration, constrained RAM for entropy buffering, and infrequent reseeding due to limited write cycles or system design (0907.1256).
Predictability and State Compromises: Software PRNGs seeded from predictable or low-entropy events are easily brute-forced, as with analogRead() seeds in Atmel-based Arduinos (recoverable in seconds from output traces) (Kristinsson, 2012).
PRNG Fallbacks: When the HRNG pathway fails or is untested, firmware commonly falls back to "synthetic" PRNGs (e.g., CRC32-mixed hardware registers in Bluetooth), which empirically provide ≤18 bits entropy per 32-bit output and fail Dieharder/NIST SP800-22 tests (Tillmanns et al., 2020).

Observable symptoms are protocol breakage (predictable keys/nonces), statistical test failures, or outright cryptographic compromise.

2. Physical and Algorithmic Entropy Sources

A taxonomy of entropy sources in embedded platforms includes:

Source Type	Theoretical/Empirical Min-Entropy	Noted Limitation(s)
Stochastic MTJ	H→1 bit/sample (with adaptation) (Zahoor et al., 7 Jul 2025)	Biased under PVT without adaptation
SRAM Startup	~0.103 bits/bit (0907.1256); 0.46 bits/byte (Grycel et al., 2019)	Data remanence, long power-off needed, small throughput
Oscillator Jitter	1.19–1.47 bits/8-bit sample (Grycel et al., 2019)	Reduces under temperature drift, requires health monitoring
Temperature Noise	~2.93 bits/8-bit sample (Grycel et al., 2019)	Needs high-integrity ADC; continuous entropy only at high sampling
DRAM Activation Failures	≥0.95 bits/sample (Kim et al., 2018)	Entropy only for selected cells; needs temperature-aware profiling
Analog-Pin Noise	≤10 bits per 1024 values but much lower in practice (Kristinsson, 2012)	Highly predictable, host-dependent, fails FIPS/NIST tests
Firmware/CPU Jitter (SideRand)	≥6.67 bits/sample (Roig, 2018)	Requires high-resolution timers
Chaotic ADC Feedback	≈0.99 bits/bit post-XOR (Fabbri et al., 2014)	Analog bias possible; requires calibration
5G Spectral Features	≥0.6·ℓ bits/window (Catak et al., 2023)	Requires continuous RF reception/decoding infrastructure

A consistent conclusion is that naive analog and CPU-based approaches on commodity Arduino-class hardware produce insufficient entropy for cryptographic operations [(Kristinsson, 2012); (0907.1256)].

3. Sources of Bias, Drift, and Failures: Empirical Evidence

Weaknesses in embedded RNGs manifest as statistical bias (Pr[0] ≠ Pr[1]), runs or patterns, and failing entropy rates in standard test batteries. Examples:

Physical Drift and Process Variation: With sMTJs, bias in the mean conductance or shift in TMR ratio causes comparator fixed-threshold schemes to deviate from the ideal 0.5/0.5 balance in output bits; the adaptive digitizer in (Zahoor et al., 7 Jul 2025) corrects this by tracking the slow mean and recentering the threshold dynamically, eliminating bias due to drift.
Data Remanence in SRAM: Data stored in SRAM persists for 15–30 s post-power-off; if not fully decayed, outputs are deterministic—radio-frequency attacks and power-hold by adversaries can force repetition (0907.1256).
Fallback to Deterministic Algorithms: Broadcom Bluetooth PRNG fallback uses CRC32 on a small set of hardware registers and clocks, yielding outputs that pass neither Dieharder nor have more than ≈18 bits entropy per 32 bits. Cryptographic material generated from such sources can be brute-forced in ≈2²⁶ trials for 128 bits (Tillmanns et al., 2020).
Environmental Variability: Oscillator jitter and temperature noise entropy rates fall when environmental change affects phase or ADC readings are autocorrelated; practical systems halt reseeding in such intervals (Grycel et al., 2019).

Comprehensive test results demonstrate failure modes:

Without adaptation/post-processing, many embedded-class RNGs fail NIST SP800-22 monobit, runs, and entropy tests under drift or insufficient entropy [(Zahoor et al., 7 Jul 2025); (Kristinsson, 2012); (Tillmanns et al., 2020)].

4. Countermeasures: Architectures for Resilient and High-Entropy RNGs

Recent research demonstrates multiple architectural remedies to weak RNGs:

Adaptive Digitizers: By incorporating a low-pass filter front-end that tracks the mean of the entropy source and re-centers the comparator, moving-threshold digitizers maintain unbiased outputs even under severe supply/process drift (e.g., passing all 16 NIST tests across ±75% G₀, ±17% TMR, 3–6.5 V V_DD) (Zahoor et al., 7 Jul 2025).
Entropy Pooling and Health Tests: Fortuna-like accumulators combine ISR-triggered events (oscillator, temperature, SRAM) and require a minimum entropy budget prior to reseeding the cryptographic generator, preventing single-source dependency and enforcing periodic cryptographic renewal (Grycel et al., 2019, Kietzmann et al., 2020).
Hardware Fault Exploitation: TuRaN and D-RaNGe demonstrate exploiting access or activation failures in memory-by-design: DRAM under reduced tRCD and SRAM under undervolt, both producing high-entropy outputs verified by NIST (Kim et al., 2018, Yüksel et al., 2022).
Side-Channel Entropy Harvesting: SideRand transforms the microarchitectural timing jitter—impacting trivial arithmetic loops under varying cache/pipeline/memory conditions—into a software-only digital entropy source, robust above 6 bits/sample under conservative conditions, without the need for analog hardware (Roig, 2018).
Post-Processing Extractors: Universal hash extractors, von Neumann correctors, and lightweight ciphers (Mini-Trivium, SHA-256) remove bias/correlation in raw bitstreams with verified statistical guarantees (Zahoor et al., 7 Jul 2025, Grycel et al., 2019, Catak et al., 2023). XOR-whitening is shown to suffice for sub-10 USD chaotic-feedback TRNGs in retrofitting settings (Fabbri et al., 2014).

5. Performance, Security, and Integration Benchmarks

Key trade-offs in embedded RNG adoption span area, power, throughput, and integration complexity.

RNG/Technique	Throughput	Power/Area	Post-Processing	Min-Entropy/Validation	Notable Integration Requirements
sMTJ + Adaptive	~1.0–1.7 Mb/s (Zahoor et al., 7 Jul 2025)	<1 μW (core), <50 μm² logic	Mini-Trivium/Trivium (~20–2,000 gates)	1 bit/sample; NIST SP800-22 pass	Adaptive voltage reference, 1st-order LPF
DRAM D-RaNGe	40–180 Mb/s/channel (Kim et al., 2018)	4.4 nJ/bit (DRAMPower model)	Optional XOR/Neumann	≥0.95 bit/sample; NIST pass	MC timing reg; cell profiling
TuRaN (SRAM)	1.6–1.8 Gb/s (Yüksel et al., 2022)	0.11 nJ/bit	SHA-256 (on-chip, pipelined)	NIST pass; ~8 bits/32b block	Fine-grain voltage control at macro line
ERHARD-RNG	41.8 kb/s entropy injection (Grycel et al., 2019)	~6 KB code, ~1 KB data	Fortuna (SHA-256/AES-CTR)	4.4 bits/sample; SP800-90B/NIST	Accumulator pools; hardware event ISRs
SideRand (CPU)	124–1,600 bit/s (Roig, 2018)	<1 kB RAM, 100% CPU during	SHA-256	≥6–7 bits/sample; MFV bound	High-res timer, CPU burst period
Chaotic ADC	32–100 kbit/s (Fabbri et al., 2014)	~100 mW; <2 kB code	XOR/Von Neumann	≥0.99 bits/bit, NIST pass with whitening	Add-on SPI/I2C/USB module

Security analyses consistently require that post-extraction entropy and randomization be empirically validated via NIST SP800-22 or Dieharder. Fallback-only or insufficient entropy (e.g., CRC32 PRNG) are documented as exploitable in real Bluetooth stacks (Tillmanns et al., 2020).

6. Design Recommendations, Best Practices, and Remaining Weaknesses

Best-practice recommendations synthesized from cross-paper analysis include:

Do not rely exclusively on a single entropy source: Use at least two physically independent processes (e.g., SRAM and oscillator jitter) for seed initialization and periodic reseeding (Grycel et al., 2019, Kietzmann et al., 2020).
Incorporate adaptive analog/digital front-ends: Drift-tracking digitizers or dynamic parameter calibration mitigates process and supply sensitivity, maintaining unbiased outputs in sMTJ, DRAM, or SRAM mechanisms (Zahoor et al., 7 Jul 2025, Yüksel et al., 2022).
Mandatory entropy estimation and health testing: Explicitly track per-sample min-entropy, implement on-boot self-tests (known-answer for ciphers/hashes), and health-test TRNG output continuously at runtime (Kietzmann et al., 2020).
Avoid deterministic PRNG fallbacks and weak conditioning: Systems must not fall back to LCG or CRC32-mixed PRNGs for cryptographic material; always prefer cryptographically secure DRBGs seeded from proven entropy pools (Tillmanns et al., 2020).
Lightweight integration: For legacy or ultra-constrained systems, retrofittable modules leveraging chaotic nonlinear maps or exploiting existing analog hardware can deliver secure entropy at low cost (<$10), validated post-whitening (Fabbri et al., 2014).
Platform-specific tuning: Profile SRAM/DRAM cells or analog parameters per chip/temperature bin to account for spatial/temporal variation in entropy quality [(Kim et al., 2018); (Yüksel et al., 2022); (0907.1256)].
Minimal state exposure: Isolate and zeroize generator state, employ memory protection, and physically harden critical TRNG/PRNG pathways (Kietzmann et al., 2020).

Unresolved weaknesses are present for ultra-low-memory platforms (e.g., RFID with <144 B SRAM)—data remanence and low entropy rates may be unremediable without additional hardware support (0907.1256). For MCUs lacking a hardware RNG, only aggressive entropy harvesting (e.g., SideRand or external module) provides practical assurance.

7. Perspectives and Research Trajectory

Recent progress in embedded RNGs demonstrates viability for high-throughput, low-power, robust entropy generation by exploiting new physical effects (e.g., sMTJ, DRAM/SRAM faults), algorithmic adaptation, and multi-source pooling. Mainstream platforms increasingly integrate HW TRNG IP blocks with built-in health monitors (Kietzmann et al., 2020), but retrofits for legacy, fielded, or ultra-constrained nodes remain a research frontier. Continuing directions include more granular adaptive correction schemes for process-drifts, formal composability of multi-entropy pooling, on-chip noise characterizers, and open validation suites for embedded environments.

Weak or biased random number generation remains a principal risk for the integrity of cryptographic operations in embedded systems, but the confluence of physical, algorithmic, and architectural countermeasures now enables robust, certifiable designs for a broad range of IoT and edge devices (Zahoor et al., 7 Jul 2025, Yüksel et al., 2022, Grycel et al., 2019, Kietzmann et al., 2020, Tillmanns et al., 2020).