From this web page screenshot, the following key information about the vulnerability can be extracted: 1. **Vulnerability Overview** - ShadowMQ: A critical vulnerability spreading through code reuse. - Affected Scope: Impacts projects including Meta, NVIDIA, Microsoft, vLLM, SGLang, and Modular. 2. **Root Cause of Discovery** - While analyzing Meta’s Llama Stack, it was found that ZMQ’s `recv_pyobj()` method was used to deserialize incoming data. - The `pickle` module in Python can execute arbitrary code and is unsuitable for exposure over networks. - Multiple projects contain nearly identical insecure patterns due to code reuse: pickle deserialization over unauthenticated ZMQ TCP sockets. 3. **Affected Frameworks** - NVIDIA’s TensorRT-LLM - PyTorch’s vLLM and SGLang - Modular’s Max Server 4. **Potential Attack Surface** - Through a single vulnerable node, attackers can execute arbitrary code, escalate privileges, steal model data or secrets, install GPU-based miners, etc. 5. **Patch Timeline** - October 2024: Meta Llama Stack (CVE-2024-50050) - May 2025: vLLM (CVE-2025-30165) - May 2025: NVIDIA TensorRT-LLM (CVE-2025-23254) - June 2025: Modular Max Server (CVE-2025-60455) 6. **Mitigation Recommendations** - Patch immediately - Avoid using `pickle` or `recv_pyobj()` for handling untrusted data - Implement authentication mechanisms - Scan and isolate exposed ZMQ endpoints - Educate development teams on secure serialization practices ## Key Vulnerability CVEs: - CVE-2024-50050 - CVE-2025-30165 - CVE-2025-23254 - CVE-2025-60455