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Researchers demonstrate hidden backdoors in open-source LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have demonstrated that open-source Large Language Models (LLMs) can harbor hidden backdoors, even after fine-tuning or merging with other models. These vulnerabilities are difficult to detect and can be intentionally introduced into models. A proof-of-concept backdoor was successfully created to illustrate the ease with which such malicious modifications can be implemented. AI

IMPACT Highlights significant security risks in open-source LLMs, potentially impacting trust and adoption.

RANK_REASON Demonstration of a security vulnerability in open-source LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Mastodon — mastodon.social →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Researchers demonstrate hidden backdoors in open-source LLMs

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6 / 100
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Demonstration of a security vulnerability in open-source LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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safety, model release
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High
Clearly on-topic for AI-industry coverage.
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Breaking (< 6h)
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COVERAGE [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · [email protected] ·

    LLMs can contain nasty backdoors AND YOU CAN'T FIND THEM Any open model whose weights have been edited can carry a backdoor, whether it's a task-specific fine-t

    LLMs can contain nasty backdoors AND YOU CAN'T FIND THEM Any open model whose weights have been edited can carry a backdoor, whether it's a task-specific fine-tune, a merged adapter or an abliterated build. We built one to see what it takes, https:// projectdiscovery.io/research/…

    LINKS projectdiscovery.io/…/how-abliterated-mod…

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