Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves.
arXiv:2211. 00111v3 Announce Type: replace-cross Abstract: Rust, as an emerging system programming language, introduces $\texttt{unsafe}$ to allow developers to bypass safety checks during compilation.
arXiv:2603. 18334v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) increasingly assist secure software development, their ability to meet the rigorous demands of Rust program verification remains unclear.
Applying blockchain primitives to dataset versioning, provenance, and integrity assurance The post Ensuring Data Integrity with Cryptographic Hashing and the Ethereum Blockchain appeared first on Towards Data Science .
arXiv:2606. 23768v1 Announce Type: cross Abstract: We propose cryptographic certificates of validity for agentic AI systems.
arXiv:2502. 02068v3 Announce Type: replace-cross Abstract: This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid intellectual property rights violations and inappropriate misuse in software development.
arXiv:2607. 04729v1 Announce Type: cross Abstract: LLM agents are increasingly applied to vulnerability analysis, but existing benchmarks have not kept pace.
arXiv:2607. 14340v1 Announce Type: cross Abstract: AI coding agents produce code faster than humans can review it.
Scratchy introduces a visual-scratchpad method for generating cryptographic proofs in EasyCrypt by converting natural-language security descriptions into a typed proof-relation graph and then into a visual proof state that guides multimodal language models. The approach exposes implicit proof-theoretic dependencies that LLMs struggle with, enabling clearer coordination of probability, adversarial games, invariants, assumptions, and bounds. Scratchy-eval, a 114-task dataset from official EasyCrypt files, demonstrates that classical LLMs achieve significant gains when using these structured visual proof states.
arXiv:2604. 03750v2 Announce Type: replace-cross Abstract: Reverse engineering (RE) is central to software security, particularly for cryptographic programs that handle sensitive data and are highly prone to vulnerabilities.
GPT-5. 2-Codex is OpenAI’s most advanced coding model, offering long-horizon reasoning, large-scale code transformations, and enhanced cybersecurity capabilities.
arXiv:2603. 05786v2 Announce Type: replace-cross Abstract: As AI agents become widely deployed as online services, users often rely on an agent developer's claim about how safety is enforced, which introduces a threat where safety measures are falsely advertised.
arXiv:2607. 21839v1 Announce Type: cross Abstract: Privacy-preserving machine learning auditing protocols allow auditors to assess models for properties such as accuracy or fairness, without revealing their internals or training data.