AICrypto: AI-Enabled Cryptography & Benchmarks

• AICrypto is a multifaceted domain that integrates AI with cryptographic designs, enabling systems to resist both classical and quantum-enabled attacks.
• It leverages advanced encryption techniques, including pattern-devoid ciphers, physical randomness sources, and FPGA implementations to ensure high security and performance.
• The field encompasses expert-curated benchmarks for evaluating LLM reasoning and secure multi-agent protocols, guiding improvements in cryptographic methods.

AICrypto encompasses a set of technologies, frameworks, and benchmarks at the intersection of artificial intelligence and cryptography. The term refers both to advances in cryptography necessitated or enabled by AI, and to expert-curated benchmarks designed to evaluate cryptographic reasoning in LLMs and AI systems. These developments span the design of cryptosystems robust against AI- and quantum-AI-powered attacks, methodologies for training and evaluating LLMs on cryptographic problems, and secure-by-design protocols leveraging AI in multi-agent environments.

1. AICrypto Systems: Threat Models and Cryptanalytic Context

AICrypto systems are designed under threat models that assume adversarial capabilities driven by advanced AI engines and Quantum AI (QAI) enhancements. These adversaries leverage persistent predictive inference, deep learning-based pattern recognition, and can learn mappings from sparse plaintext–ciphertext pairs. Notably, plaintext-originating attacks become feasible wherein attackers precompute high-probability plaintext lists ($A^*=\{A_1,\,A_2,\ldots,A_n\}$) and correlate intercepted ciphertext $C$ with likely $A_i$ using ML/DL classifiers. Quantum-AI further accelerates brute-force and probabilistic search, shrinking search complexity by $\sqrt{2^k}$ or better, and enhances side-channel correlation via quantum-enabled parallelization.

AICrypto thus aims to preclude attacks that exploit constrained message space or statistical leaks, denying reliable inference of mappings $C\to A$ for both classical and quantum-enabled attackers (Harris et al., 2023).

2. Cryptographic Primitives and the AICrypto Framework

AICrypto advances both symmetric and asymmetric cryptography, with special emphasis on ensuring security in AI-driven, multi-agent environments. Key strategies include:

• Pattern-Devoid, Randomness-Rich Ciphers: Family of trans-Vernam, decoy-tolerant ciphers employing BitFlip (one-to-many Hamming-distance mappings) and BitMap (high-dimensional lattice shuffles) primitives. These designs inject decoys and obfuscate cipher structure, undermining deep learning classifiers.
• Non-Algorithmic Randomness: Entropy sourced from physical TRNGs (e.g., avalanche noise, ring oscillators), subjected to NIST SP-800-90B testing, achieving $\geq 0.9$ bits of entropy per bit. Reseeding policies and health tests maintain entropy integrity.
• Large-Key Entropy and Unicity Distance: Let $H(K)$ denote key entropy and $D_p$ plaintext redundancy. Setting $U=H(K)/D_p$ (with $U$ the unicity distance) ensures no unique key/plaintext arises even with extensive ciphertext collection, provided $C$0 (for $C$1 symbols).
• Trade-Offs and Performance: FPGA implementations with $C$2–$C$3 bits achieve unicity distances of up to $C$4 symbols, with throughput of 100–120 Mbps and bandwidth expansion $C$5 due to decoy injection. Latency and overhead scale sub-linearly with key size, dominated by TRNG sampling and memory lookup (Harris et al., 2023).

Hardware-anchored key storage (128 Gb PCM/NVDIMM), post-quantum key exchange (e.g., Kyber), and one-time or limited-use sub-keys are foundational in AICrypto designs.

3. Integration of Cryptography in AI-Driven Systems

Symmetric (AES) and asymmetric (RSA) schemes remain foundational within AI-driven cybersecurity, as detailed in comparative studies (Kshetri et al., 2024):

• Symmetric Encryption (AES): Optimized for high-throughput, low-latency use cases (e.g., IoT telemetry, video analytics), particularly when hardware acceleration (AES-NI, FPGA) is available. Limitation arises in large-scale key management.
• Asymmetric Encryption (RSA): Critical for secure initialization (e.g., federated learning seed exchanges), digital signatures, and trust bootstrapping. However, RSA suffers from high key-generation and per-message computational costs, making it suitable for small payloads or key exchange only.

A hybrid approach, wherein AES encrypts bulk payloads and RSA secures key distribution, is prevalent. AI-augmented solutions leverage ML models for anomaly detection in encrypted streams, dynamic key management (k-NN, genetic optimization), and real-time adaptive cryptosystem configuration.

Performance metrics from recent evaluations:

Aspect	AES (Symmetric)	RSA (Asymmetric)
Throughput	$C$6300 MB/s	1–5 MB/s (2048-bit)
Latency	1–2 ms/MB	100–200 ms/256B
Key Generation	$C$70.1ms	50–200ms (2048-bit)
Scalability	Bulk data streams	Decentralized networks

AES: $C$8; RSA: $C$9 for key length $A_i$0 (Kshetri et al., 2024).

4. AICrypto Benchmarks: Datasets and Evaluation

The AICrypto Benchmark (Wang et al., 13 Jul 2025) is the first large-scale, expert-reviewed suite for comprehensive cryptography assessment in LLMs, comprising:

• MCQs: 135 questions (118 single-answer, 17 multi-answer) spanning mathematical foundations, classical ciphers, symmetric/asymmetric-key schemes, and protocols.
• CTF Challenges: 150 tasks (73 static, 77 dynamic) in areas such as classical and modern ciphers, hash functions, DLP, lattice and ECC, and “homemade” cryptographic schemes.
• Proof Problems: 18 formal proof tasks (encryption/sig correctness, pseudorandomness, hybrid argument, adversary advantage).
• Agent-Based Evaluation Framework: Automated harness executes LLM “reasoning–action–content” formats in sand-boxed environments, producing pass@3 (for CTF challenges) and grading MCQs/proofs against human expert baselines.

Performance baselines:

Model	MCQ Acc. (%)	CTF pass@3 (%)	Proof Score (%)
Human Expert	77.5	81.2	88.1
Top LLMs	95–98	54–55	84–85
Median LLM	92	30	58

LLMs excel in conceptual recall (MCQ), routine proofs, and known-vulnerability exploitation, but exhibit deficits in deep mathematical reasoning, multi-step dynamic protocols, and strategic CTF planning (Wang et al., 13 Jul 2025).

The "AICrypto MCQ" subset (135 questions) is also employed by RL fine-tuning studies (e.g., GRPO-trained Llama-3.1-8B). Curriculum-based RL improves Pass@8 from 4% (baseline) to 19%, Maj@8 to 7%, evidencing transfer of procedural reasoning to format-agnostic settings (Muzsai et al., 1 Jun 2025).

5. Privacy and Secure Computation in AI Agents

AgentCrypt extends AICrypto principles to multi-agent, privacy-compliant AI communication (Karthikeyan et al., 8 Dec 2025). The framework provides:

• Four Cryptographic Privacy Levels: ranging from plaintext (Level 1), to policy-encrypted (Level 2), policy-enforced computation (Level 3), and fully homomorphic evaluation (Level 4) supporting operations such as sum, min, max.
• Formal Security Guarantees: Simulation-based privacy for Level 2/3; Level 4 offers IND-CPA security under CKKS-style FHE.
• Benchmarking: 300+ scenarios for regulated domains, measuring privacy leakage (0% for Level 4), and decision-making error rates ($A_i$15.5% wrong database/tool selection).
• Implementation: Drop-in security wrappers for graph-based (Langgraph) or agent2agent (Google ADK) runtimes, with enforcement at the message/tool edge or RPC call.

AgentCrypt ensures privacy holds regardless of agent behavior, a crucial property as LLMs are inherently probabilistic and heuristic. However, correctness (choice of function/tool) is not cryptographically enforced. FHE overhead remains within practical bounds (encryption/decryption O(10ms) per 1k rows; FHE eval 5–128ms for 100–3,000 rows, AWS r5.xlarge) (Karthikeyan et al., 8 Dec 2025).

6. AI for Cryptography: Detection and Classification

"Deep Learning Based Cryptographic Primitive Classification" (Hill et al., 2017) demonstrates AI methods classifying cryptographic primitives in binary executables using a DCNN over dynamic execution control-flow traces. The CryptoKnight system procedurally generates binaries implementing AES, RC4, Blowfish, MD5, and RSA, with multivariate obfuscations and compiler diversity, delivering 91.3% classification accuracy. The system leverages:

• Control-flow feature extraction (opcode counts, Shannon entropy on memory writes)
• Dynamic k-max pooling for variable-length traces
• Robustness to obfuscation/polymorphism

This supports malware detection, reverse engineering, and automated analysis of cryptovirological threats. Limitations include difficulty in detecting novel primitives and processing hardware-accelerated code (e.g., AES-NI) (Hill et al., 2017).

7. Quantum-Resistance and Future Directions

AICrypto frameworks align with post-quantum guidelines by:

• Utilizing large keys and entropy sources that bypass algebraic structures vulnerable to QAI/ML attacks.
• Introducing ciphers with pattern-devoid, randomness-rich architectures.
• Supporting integration with PQC protocols (e.g., Kyber, QKD) for key distribution.
• Investigating AI-guided cryptosystem adaptation: reinforcement learning for key rotation, LSTM/CNN for round-key scheduling, and noise management in FHE (Kshetri et al., 2024).

A plausible implication is that AI itself can be leveraged both in attack (adaptive cryptanalysis) and defense (anomaly detection, dynamic adaptation), necessitating a continuous co-evolution of techniques at this interface.

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AICrypto thus denotes a technically multifaceted domain at the convergence of cryptography and AI, spanning robust system/algorithm design, empirical benchmarking of AI reasoning in cryptography, and the operationalization of cryptography in privacy-preserving, multi-agent AI ecosystems (Harris et al., 2023, Kshetri et al., 2024, Wang et al., 13 Jul 2025, Muzsai et al., 1 Jun 2025, Hill et al., 2017, Karthikeyan et al., 8 Dec 2025).