Results for “"hallucinate"”
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The Register
4 months ago
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1–10 of 15 articles
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The Register
The Hacker News
Large language models keep inventing web addresses that do not exist. Attackers have started buying those made-up domains before anyone else can, then hosting phishing pages on them to catch traffic that AI tools point their way. Palo Alto Networks' Unit 42 calls the trick phantom squatting, and its new research shows it is already happening in the wild. The reason it matters is
Dark Reading
The hallucination problem is not just pervasive, it is persistent as well, according to new research.
Bleeping Computer
A new class of supply chain attacks named 'slopsquatting' has emerged from the increased use of generative AI tools for coding and the model's tendency to "hallucinate" non-existent package names. [...]
The Register
Simply look out for libraries imagined by ML and make them real, with actual malicious code. No wait, don't do that Several big businesses have published source code that incorporates a software package previously hallucinated by generative AI.… In-depth
Dark Reading
Incident response triage and software vulnerability discovery are two areas where the large language model has demonstrated success, although false positives are common.
Bleeping Computer
AI coding tools can introduce unvetted or hallucinated open source dependencies faster than traditional security reviews can keep pace. ActiveState explains why organizations should govern packages at the point of selection, before they enter the development pipeline. [...]
Bleeping Computer
Slopsquatting, phantom squatting, and HalluSquatting all exploit the same late-binding attack pattern, where AI coding agents trust hallucinated package, repo, or domain names. ActiveState explains how pre-fetch verification and governed dependency management can help stop these attacks before malicious code enters the pipeline. [...]
Dark Reading
LLMs consistently hallucinate Web domains for legitimate brands that attackers can register for malicious activity in a difficult-to-detect attack vector.
Dark Reading
AI models often hallucinate or make costly mistakes when tasked with recommending software versions, upgrade paths, and security fixes — leading to significant technical debt.