CryptoBench: Advanced Crypto Benchmarking

Updated 4 December 2025

CryptoBench is a comprehensive suite of benchmarking frameworks designed to evaluate cryptographic algorithms, systems, and LLM agents in decentralized finance and crypto analysis.
The framework employs dynamic, expert-curated tasks across retrieval and prediction quadrants, providing precise, reproducible metrics for LLM performance under real-world crypto scenarios.
Microbenchmarks and static analysis tests in CryptoBench offer detailed performance insights and vulnerability detection, with evaluations showing significant speedups and challenges in advanced cases.

CryptoBench is a collective term for several advanced benchmarking suites and frameworks targeting rigorous, expert-grade evaluation of algorithms, systems, and agents in the cryptography and cryptocurrency domains. The term encompasses tools for static analysis of cryptographic API usage, dynamic benchmarking for LLM agents in crypto analysis, compositional reasoning evaluation in LLMs, and microbenchmarking of cryptographic primitives. CryptoBench benchmarks address the unique operational, analytical, and security challenges posed by financial cryptography, decentralized systems, and emerging AI-based tools.

1. Scope and Motivation

CryptoBench was introduced to fill critical gaps left by general-purpose agent, reasoning, and cryptography benchmarks, which insufficiently capture crypto-native requirements such as extreme time-sensitivity, adversarial information flows, compositional problem structure, and the need for fine-grained analysis of low-level primitives and real-world API misuses. The CryptoBench framework provides living/dynamic, expert-curated tasks for LLM evaluation (Guo et al., 29 Nov 2025), cryptographic API misuse detection in Java (Afrose et al., 2021), compositional reasoning stress tests for LLMs via encoded transformations (Shi et al., 8 Feb 2025), and standardized performance microbenchmarks for hashing and symmetric/asymmetric cryptography (Pandya, 11 Jul 2024). It is designed to enable reproducible, fine-grained, and domain-relevant measurement of systems and algorithms in modern cryptography and cryptocurrency analytics.

2. Dynamic Expert-Curated Benchmarking in Cryptocurrency Analysis

The dynamic CryptoBench for LLM agent evaluation (Guo et al., 29 Nov 2025) is designed around a live, monthly-refresh architecture. Each month, a committee of crypto-native professionals drafts, validates, and adjudicates 50 new tasks that mimic actual analyst workflows in DeFi, on-chain intelligence, and market prediction. Question templates are instantiated programmatically using current protocol statistics, addresses, and on-chain events. The benchmark's four-quadrant taxonomy distinguishes:

Simple Retrieval (SR): Single-fact lookup (e.g., TVL extraction from DEX dashboards)
Complex Retrieval (CR): Multi-source, multi-entity extraction (e.g., cross-referencing on-chain identities with metrics)
Simple Prediction (SP): Univariate or heuristic inference (e.g., price impact of token unlocks)
Complex Prediction (CP): Multi-step reasoning combining synthesis, forecasting, and risk assessment (e.g., protocol risk ranking conditioned on vulnerabilities and sentiment)

Scoring is performed by an LLM-as-Judge rubric (0–3 scale), with numerical answers required to be within ±5% tolerance. Separate success rates are maintained for each quadrant, exposing systemic retrieval-prediction imbalances: current top-tier LLMs score >40% in SR but typically <10% in CP tasks, demonstrating analytic deficiency despite proficiency in fact extraction.

3. Static Analysis Benchmarks for Cryptographic API Misuse

CryptoBench includes comprehensive suites for evaluating the accuracy and robustness of static analysis tools in detecting cryptographic API misuse (Afrose et al., 2021). The principal components are:

CryptoAPI-Bench: 181 unit tests (144 ground-truth misuses, 37 secure cases), covering basic cases and advanced flows such as interprocedural, field-sensitive, multi-class, and path-sensitive scenarios.
ApacheCryptoAPI-Bench: 121 real-world misuse/secure cases collected across 10 Apache projects, annotated with file/line details and precise descriptions of misuse patterns (e.g., hard-coded keys, insecure cipher modes, missing hostname verification).

Benchmarked tools include SpotBugs, CryptoGuard, CrySL, and Coverity SAST, each evaluated via rigorous metrics: Precision, Recall, F1, and False-Positive-Rate, with scalability assessed via runtime on large codebases. CryptoGuard dominates recall but none of the tools support path-sensitivity or perfect advanced-case coverage.

Suite	Test Cases	Insecure	Secure	Dimensions Covered
CryptoAPI-Bench	181	144	37	Interproc, Field, Path, Multi-class
ApacheCryptoAPI-Bench	121	88	33	Advanced cases from 10 projects

4. Compositional Reasoning Stress Tests for LLMs

CryptoBench incorporates the compositional reasoning framework CryptoX (Shi et al., 8 Feb 2025), which systematically transforms existing NLP, math, and code benchmarks into cryptographically encoded versions. Prompts are converted via word-level ciphering and mapping steps, forcing multi-stage answer extraction: decoding ciphertext, solving the base task, and applying secondary mappings. Benchmarks covered include Crypto-MATH, Crypto-MMLU, Crypto-BBH, and Crypto-MBPP. Model scores are aggregated across difficulty levels by the area-under-curve (AUC) over the number of encrypted words, producing a quantitative measure of compositional reasoning robustness.

Closed-source LLMs consistently achieve higher AUC and maintain accuracy at medium/high encryption, while open-source models drop sharply, indicating real gaps in generalization and decomposition abilities. Mechanistic interpretability analyses highlight staged progressions in neuron activation and output distributions, mapping subproblem solution and synthesis phases to model layers.

5. Microbenchmarking of Cryptographic Primitives

CryptoBench provides a standardized protocol for performance comparison of cryptographic primitives, exemplified by cross-platform hashing suite evaluations (Pandya, 11 Jul 2024). Tests cover SHA-256, SHA-512, and BLAKE3, implemented in C and benchmarked on MacBook Pro, high-core cloud VMs, and AMD EPYC servers. Core metrics include throughput (MB/s), latency per block, peak memory usage, and parallel efficiency. BLAKE3 achieves >10× throughput and 5× lower memory footprint versus SHA-2, with linear parallel speedup up to 32–64 threads. Recommendations are drawn for mining, verification, and legacy environments, with extensibility to symmetric ciphers, MACs, asymmetric primitives, and KDFs.

Algorithm	Single-thread MB/s	Latency (ms)	Memory (MiB)	Speedup vs SHA-256
SHA-256	250	4.00	9.8	–
SHA-512	180	5.56	10.6	–
BLAKE3	3100	0.32	1.9	+1140%

6. Evaluation Protocols and Metrics

CryptoBench employs consistent evaluation methodologies tailored to benchmark type:

LLM-agent CryptoBench: Monthly dynamic task pool, expert adjudication, 0–3 scoring rubric, quadrant-level success rates, paired t-test for score significance (Guo et al., 29 Nov 2025).
CryptoAPI/ApacheCryptoAPI-Bench: Unit/macro tests, ground-truth annotation, formal definitions: Precision, Recall ( $\displaystyle \frac{TP}{TP+FN}$ ), F1, False-Positive-Rate ( $\displaystyle \frac{FP}{FP+TN}$ ), per-category and advanced-case breakdowns (Afrose et al., 2021).
CryptoX Reasoning: Exact match, LLM-as-Judge, UnitTest, and AUC over encoding levels—the latter offering superior discrimination of compositional skill (Shi et al., 8 Feb 2025).
Hash microbenchmarks: Throughput ( $T = \text{Total bytes}/\text{wall-clock time}$ ), latency, memory, parallel speedup ( $S_p = T_p/T_1$ ), and efficiency ( $\eta_p=S_p/p$ ) (Pandya, 11 Jul 2024).

7. Limitations and Future Directions

CryptoBench benchmarks reveal domain-specific bottlenecks and agent weaknesses: LLMs underperform in analytic/prediction tasks and exhibit source fidelity/caching errors (Guo et al., 29 Nov 2025); static analysis tools fail to handle complex flows and path sensitivity (Afrose et al., 2021); open-source LLMs lack robustness under compositional transformations (Shi et al., 8 Feb 2025). The suite proposes actionable improvements—domain-specific toolkits, enhanced agentic planning, multi-step training regimes, and continual feedback integration. Suggested extensions include automation of pipeline scripts, novel cryptographic algorithms, broader language and platform support, and adaptive benchmark expansion based on emerging narratives and market events.

CryptoBench serves as a rigorous, extensible benchmark infrastructure for frontier research in cryptographic software engineering, privacy-preserving computation, agentic LLM deployment, and algorithmic trading, supporting reproducible, high-resolution measurement across the diverse landscape of financial cryptography and decentralized analytics.