
Malicious Python packages target popular Bitcoin library
RL researchers detected two Python libraries that are designed to steal sensitive data while posing as fixes for a popular cryptocurrency library.

RL researchers detected two Python libraries that are designed to steal sensitive data while posing as fixes for a popular cryptocurrency library.

For the first time, RL researchers discover malicious locally-installed npm packages infecting other legitimate packages.

Software development teams working on machine learning take note: RL threat researchers have identified nullifAI, a novel attack technique used on Hugging Face.

To avoid compromised packages being introduced as a dependency in a larger project, security teams need to keep an eye peeled for such malicious code.

Hacks of rspack, vant highlight the growing trend of cryptomining compromises spreading via top open-source packages.

A compromised build environment led to a malicious deployment of a popular AI library that had the potential of delivering other malware.

Two recent versions of the Solana web3.js open source library were infected with code to steal private keys, putting crypto platforms and wallets at risk.

The incident demonstrates how machine learning-based threat hunting can help development teams spot threats other tools miss.

Three versions of the popular package were infected and used to spread malicious code that was stealing crypto wallet assets.
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RL found the VMConnect campaign continuing with malicious actors posing as recruiters, using packages and the names of financial firms to lure developers.

Malware authors upped their game, using homoglyphs to impersonate a protected NuGet prefix and IL weaving to inject malicious code, RL researchers found.

The history of the package is a lesson in why tracking open source threats is such a challenge — and highlights the value of RL's new Spectra Assure Community.

RL discovered what appeared to be a malicious downloader on PyPI. It turned out to be red teaming — but highlights a growing problem for threat detection.

There is no foolproof method to identify phony developer accounts — but there are telltale signs. Threat researchers share three.
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