CyberSecurity news
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@github.com
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A critical Remote Code Execution (RCE) vulnerability, identified as CVE-2025-32434, has been discovered in PyTorch, a widely used open-source machine learning framework. This flaw, detected by security researcher Ji’an Zhou, undermines the safety of the `torch.load()` function, even when configured with `weights_only=True`. This parameter was previously trusted to prevent unsafe deserialization, making the vulnerability particularly concerning for developers who relied on it as a security measure. The discovery challenges long-standing security assumptions within machine learning workflows.
This vulnerability affects PyTorch versions 2.5.1 and earlier and has been assigned a CVSS v4 score of 9.3, indicating a critical security risk. Attackers can exploit the flaw by crafting malicious model files that bypass deserialization restrictions, allowing them to execute arbitrary code on the target system during model loading. The impact is particularly severe in cloud-based AI environments, where compromised models could lead to lateral movement, data breaches, or data exfiltration. As Ji'an Zhou noted, the vulnerability is paradoxical because developers often use `weights_only=True` to mitigate security issues, unaware that it can still lead to RCE. To address this critical issue, the PyTorch team has released version 2.6.0. Users are strongly advised to immediately update their PyTorch installations. For systems that cannot be updated immediately, the only viable workaround is to avoid using `torch.load()` with `weights_only=True` entirely. Alternative model-loading methods, such as using explicit tensor extraction tools, are recommended until the patch is applied. With proof-of-concept exploits likely to emerge soon, delayed updates risk widespread system compromises.
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Jibin Joseph@PCMag Middle East ai
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DeepSeek AI's R1 model, a reasoning model praised for its detailed thought process, is now available on platforms like AWS and NVIDIA NIM. This increased accessibility allows users to build and scale generative AI applications with minimal infrastructure investment. Benchmarks have also revealed surprising performance metrics, with AMD’s Radeon RX 7900 XTX outperforming the RTX 4090 in certain DeepSeek benchmarks. The rise of DeepSeek has put the spotlight on reasoning models, which break questions down into individual steps, much like humans do.
Concerns surrounding DeepSeek have also emerged. The U.S. government is investigating whether DeepSeek smuggled restricted NVIDIA GPUs via Singapore to bypass export restrictions. A NewsGuard audit found that DeepSeek’s chatbot often advances Chinese government positions in response to prompts about Chinese, Russian, and Iranian false claims. Furthermore, security researchers discovered a "completely open" DeepSeek database that exposed user data and chat histories, raising privacy concerns. These issues have led to proposed legislation, such as the "No DeepSeek on Government Devices Act," reflecting growing worries about data security and potential misuse of the AI model.
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