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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThe reported ShadowMQ pattern is an unsafe Python object deserialization path in AI inference infrastructure: a ZeroMQ socket calls recv_pyobj(), which uses Python pickle to rebuild received objects. If an attacker can reach that socket and supply a crafted object, deserialization can run attacker-controlled code on the inference host. It is an implementation and deployment problem, not a defect in an AI model, and a framework is not automatically exposed merely because its name appears in a report.
How the reported vulnerability works
ZeroMQ provides messaging primitives, while Python’s pickle mechanism can serialize and deserialize Python objects. The recv_pyobj() convenience method combines those operations on received data. Pickle is not a safe format for untrusted input: reconstructing an object can invoke attacker-chosen Python behavior.
The practical attack chain therefore has two conditions:
- The particular framework build must use this deserialization pattern on the relevant path.
- An attacker must be able to reach the ZeroMQ socket, directly or through a network path that forwards untrusted data.
When both conditions hold, the consequence can be remote code execution with the privileges of the inference process. A socket bound only to a private, correctly segmented cluster network presents a different risk from one exposed on a public interface. Configuration, version and deployment topology decide exposure.
#1 Best Overall
Frameworks named in the reports
Cloud Security Alliance AI Safety Initiative notes, which describe themselves as AI-assisted and not officially reviewed by CSA, map the pattern to several inference projects. They also report that Oligo Security found thousands of exposed ZeroMQ sockets, including some associated with production inference deployments. Those figures are reported, point-in-time findings rather than a current independent census.
| Framework or infrastructure | Example identifier named in the notes | What is established here |
|---|---|---|
| Meta Llama Stack or related serving infrastructure | CVE-2024-50050 | The identifier is associated with the reported pattern; affected and fixed versions are not stated in the reviewed material. |
| NVIDIA TensorRT-LLM | CVE-2025-23254 | The identifier is named in the notes; use NVIDIA’s current bulletin for exact versions and mitigations. |
| Microsoft Sarathi-Serve | Not stated | The project is named as sharing the implementation pattern, but no complete CVE/version mapping is supplied. |
| vLLM | CVE-2025-30165 | The identifier is named in the notes; the reviewed material does not establish a complete affected/fixed range. |
| Modular Max Server | CVE-2025-60455 | The identifier is named in the notes; consult the vendor advisory before deciding whether a deployment is affected. |
| SGLang | Not stated | The notes attribute a comment saying an implementation was “Adapted from vLLM.” That attribution is not independent verification of the source code or of exposure. |
The notes refer to more than a dozen named RCE-class CVEs matching the broader code-reuse pattern, but they do not provide a complete, current vendor-by-vendor affected-version and fixed-version table. Do not transfer a CVE, severity, version range or patch from one framework to another.
How to check whether an inference deployment is exposed
- Inventory the software. Record every serving framework, package version, container image digest and deployment date, including sidecars and worker images. The framework name alone is insufficient; the vulnerable code may exist only in particular releases or configurations.
- Locate the IPC path. Review configuration and source for
recv_pyobj(), ZeroMQtcp://endpoints, andipc://sockets. A code search such asgrep -R --line-number "recv_pyobj" /path/to/sourcecan identify candidates, but inspect how each socket is bound and what data it accepts. - Check listening and reachable interfaces. On the host, review listeners with
ss -lntupand the service’s ZeroMQ configuration. Pay particular attention to wildcard bindings such astcp://0.0.0.0:*or an address reachable from outside the inference cluster. Test reachability only with an approved, non-destructive network assessment; do not send crafted pickle objects to production. - Trace trust boundaries. Determine whether an API, job queue, service mesh route or tenant network can forward attacker-controlled bytes to the socket. Authentication on a front-end API does not protect an independently reachable worker socket.
- Match the exact advisory. Check each project’s current security advisory for the installed version, the fixed release and any configuration workaround. The CVE examples above are leads for that review, not a substitute for vendor guidance.
What operators should do now
Patch the affected component
Apply the fixed framework or container release specified by the relevant vendor. NVIDIA’s Product Security guidance tells customers to follow the update or mitigation instructions in the applicable bulletin. If a fix is unavailable, document the exception and apply the compensating controls below while tracking the advisory.
Remove untrusted reachability
- Bind ZeroMQ IPC endpoints to private interfaces or local IPC mechanisms where the design permits.
- Block inbound access from the internet, user networks and unrelated tenants at firewalls and security groups.
- Use network policies to allow only the specific inference processes that must communicate.
- Do not assume that placing a service behind a load balancer hides a worker port; verify the effective routes and security-group rules.
Authenticate at exposed boundaries
Require strong service authentication and authorization on APIs that can cause data to be forwarded to inference workers. Treat authentication as a layer, not as permission to expose the raw ZeroMQ socket.
Rank #3
Reduce blast radius and monitor
- Run workers with the least privilege needed for model serving and isolate credentials from the host and control plane.
- Separate inference clusters from administrative and production application networks.
- Alert on unexpected ZeroMQ listeners, new wildcard bindings, unusual worker child processes and outbound connections from inference containers.
- Preserve image, package and network telemetry so a suspected compromise can be scoped without relying on memory of a mutable container.
Why “copy-paste vulnerability” needs careful interpretation
The reports describe a reused implementation pattern, including an attributed SGLang comment that says “Adapted from vLLM.” That does not prove that every derivative contains the same code, that every release is vulnerable, or that the same exploit works unchanged. Confirm the call site, socket exposure and vendor fix for each product independently.
Do not confuse this with Microsoft Semantic Kernel issues
Microsoft’s May 7, 2026 report on CVE-2026-25592 and CVE-2026-26030 concerns Semantic Kernel agent behavior: prompt injection can influence tool parameters, and unsafe framework behavior can create host risk. Those are separate vulnerabilities and a different code path. They do not establish the ZeroMQ/recv_pyobj() pattern in Sarathi-Serve or any other inference server.
How certain are the reported numbers?
The CSA notes characterize the material as unofficial AI-assisted work and as point-in-time reporting while the CVE landscape is evolving. “More than a dozen” matching RCE-class CVEs and “thousands” of exposed sockets are therefore useful indicators of scale reported by Oligo Security, not verified counts of currently exploitable installations. The notes do not establish a stronger independent count of affected versions, present-day exposure or incidents caused by this exact pattern.
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