CVE-2026-49121 (GCVE-0-2026-49121)
Vulnerability from cvelistv5 – Published: 2026-06-01 17:09 – Updated: 2026-06-30 12:09 X_Open Source
VLAI
Title
AI Tensor Engine for ROCm (AITER) 0.1.14 Unauthenticated RCE via MessageQueue.recv() Pickle Deserialization
Summary
AI Tensor Engine for ROCm (AITER) through 0.1.14 contains an unauthenticated remote code execution vulnerability in the MessageQueue.recv() function within shm_broadcast.py that allows unauthenticated remote attackers to execute arbitrary code by sending a malicious pickle payload to a ZMQ SUB socket with no authentication, HMAC, or format validation. Attackers who can reach the writer XPUB endpoint on the cluster network or supply a forged Handle with an attacker-controlled remote_subscribe_addr can deliver a crafted pickle payload that executes arbitrary code simultaneously as the inference worker process on every remote reader worker.
Severity
8.1 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://github.com/ROCm/aiter/issues/3076 | issue-tracking |
| https://github.com/ROCm/aiter/pull/3170 | issue-tracking |
| https://www.vulncheck.com/advisories/ai-tensor-en… | third-party-advisory |
| https://access.redhat.com/security/cve/CVE-2026-49121 | vdb-entryx_refsource_REDHAT |
| https://bugzilla.redhat.com/show_bug.cgi?id=2483882 | issue-trackingx_refsource_REDHAT |
| https://security.access.redhat.com/data/csaf/v2/v… | x_sadp-csaf-vex |
Impacted products
3 products
| Vendor | Product | Version | |
|---|---|---|---|
| ROCm | aiter |
Affected:
0 , ≤ 0.1.14
(semver)
|
|
| Red Hat | Red Hat AI Inference Server |
cpe:/a:redhat:ai_inference_server:3 |
|
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai |
Date Public
2026-05-07 00:00
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}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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