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      <title>fkie_cve-2026-86169</title>
      <link>https://db.gcve.eu/vuln/fkie_cve-2026-86169</link>
      <description>&lt;p&gt;Axolotl before 0.19.0 contains a remote code execution vulnerability in the multipack patch path where trust_remote_code defaults to None instead of False, causing the security guard to be bypassed. Attackers can execute arbitrary Python code by crafting a malicious Hugging Face model repository selected as base_model, which is loaded with hardcoded trust_remote_code=True during AutoModelForCausalLM.from_pretrained.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;Axolotl before 0.19.0 contains a remote code execution vulnerability in the multipack patch path where trust_remote_code defaults to None instead of False, causing the security guard to be bypassed. Attackers can execute arbitrary Python code by crafting a malicious Hugging Face model repository selected as base_model, which is loaded with hardcoded trust_remote_code=True during AutoModelForCausalLM.from_pretrained.&lt;/p&gt;</content:encoded>
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      <title>GHSA-57g9-xg4g-rmpp</title>
      <link>https://db.gcve.eu/vuln/ghsa-57g9-xg4g-rmpp</link>
      <description>&lt;p&gt;Axolotl through 0.18.0 contains a remote code execution vulnerability in the multipack patch path where trust_remote_code defaults to None instead of False, causing the security guard to be bypassed. Attackers can execute arbitrary Python code by crafting a malicious Hugging Face model repository selected as base_model, which is loaded with hardcoded trust_remote_code=True during AutoModelForCausalLM.from_pretrained.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;Axolotl through 0.18.0 contains a remote code execution vulnerability in the multipack patch path where trust_remote_code defaults to None instead of False, causing the security guard to be bypassed. Attackers can execute arbitrary Python code by crafting a malicious Hugging Face model repository selected as base_model, which is loaded with hardcoded trust_remote_code=True during AutoModelForCausalLM.from_pretrained.&lt;/p&gt;</content:encoded>
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