SShortSingh.
TopTechnologyAI & MLStartupsProgrammingScienceBusinessFinanceCrypto & Web3World
Back to feed
Programming

Anthropic's Claude Mythos AI Finds Crypto Library Flaws in TLS, AES-GCM and SSH

0
·1 views

Anthropic announced on April 7, 2026, that its Claude Mythos Preview AI model identified implementation vulnerabilities in widely used cryptographic libraries supporting TLS, AES-GCM, and SSH protocols. The findings, part of Project Glasswing — Anthropic's defensive AI-assisted security research initiative — suggest the flaws could enable certificate forgery or decryption of communications. Notably, the vulnerabilities lie in how cryptographic standards are implemented in code, not in the underlying standards themselves. One affected library, Botan, received a patch for a certificate-authentication bypass on the same day as the announcement, while two other reported vulnerabilities remained unpatched. Anthropic said it is following coordinated disclosure practices and will publish additional details as fixes become available.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingHacker News ·

Developer Criticizes Apple App Store Rating System as Hostile and Unfair

A software developer has publicly called out Apple over what they describe as a hostile rating system within the App Store. The critique, published in mid-July 2026, argues that the system is unfair to developers. The post gained traction on Hacker News, sparking discussion among the developer community. The article raises broader concerns about Apple's control over app distribution and developer treatment on its platform.

0 comments Read more at Hacker News
0
ProgrammingHacker News ·

Study Warns Scientific Literature May Corrupt LLM Training Data

A newly published analysis argues that scientific literature poses a significant risk to the quality of large language model (LLM) training datasets. The concern centers on the prevalence of flawed, retracted, or misleading research papers that LLMs may absorb as factual knowledge. When trained on such material, these models can internalize and reproduce inaccurate scientific claims with apparent confidence. The piece, published on the Reinvent Science platform, highlights this as a growing challenge for AI developers curating training corpora. The discussion has drawn attention on Hacker News, prompting debate about how to better filter academic sources used in AI training.

0 comments Read more at Hacker News
0
ProgrammingDEV Community ·

Bitweave Promises Sub-Millisecond Hybrid Vector Search Under 1.1 MB RAM

A developer has released Bitweave, an open-source hybrid retrieval engine written in Rust with Python bindings, designed for local RAG applications, edge agents, and serverless AI pipelines. The tool aims to address high memory overhead common in standard vector databases, which typically require hundreds of megabytes to gigabytes of RAM. Bitweave uses zero-copy, SIMD-accelerated architecture to handle both categorical filtering and vector search while keeping its active heap footprint under 1.1 MB RSS. Benchmarks shared by the developer show query latency under 1 millisecond, compared to roughly 18 milliseconds and 45 MB RAM usage for an indexed SQLite approach. The full benchmark suite, covering 200,000 records, is publicly available on GitHub for independent verification.

0 comments Read more at DEV Community
Anthropic's Claude Mythos AI Finds Crypto Library Flaws in TLS, AES-GCM and SSH · ShortSingh