OPENSUSE-SU-2026:11867-1
Vulnerability from csaf_opensuse - Published: 2026-09-23 00:00 - Updated: 2026-09-29 08:22Summary
python313-vllm-0.30.0-1.1 on GA media
Severity
Moderate
Notes
Title of the patch: python313-vllm-0.30.0-1.1 on GA media
Description of the patch: These are all security issues fixed in the python313-vllm-0.30.0-1.1 package on the GA media of openSUSE Tumbleweed.
Patchnames: openSUSE-Tumbleweed-2026-11867
Terms of use: CSAF 2.0 data is provided by SUSE under the Creative Commons License 4.0 with Attribution (CC-BY-4.0).
Affected products
Recommended
4 products
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: openSUSE Tumbleweed:python313-vllm-0:0.30.0-1.1.aarch64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Tumbleweed:python313-vllm-0:0.30.0-1.1.ppc64le | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Tumbleweed:python313-vllm-0:0.30.0-1.1.s390x | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Tumbleweed:python313-vllm-0:0.30.0-1.1.x86_64 | — |
Vendor Fix
|
Threats
Impact
low
4.3 (Medium)
Affected products
Recommended
4 products, the same list as for
CVE-2026-90713
Threats
Impact
moderate
5.3 (Medium)
Affected products
Recommended
4 products, the same list as for
CVE-2026-90713
Threats
Impact
moderate
4.3 (Medium)
Affected products
Recommended
4 products, the same list as for
CVE-2026-90713
Threats
Impact
moderate
References
14 references
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"text": "A security flaw has been discovered in vllm-project vLLM up to 0.29.0. The affected element is the function TiktokenTokenizer::new of the file rust/src/text/src/backend/hf/mod.rs of the component tiktoken vocab File Handler. The manipulation results in denial of service. The attack is only possible with local access. The exploit has been released to the public and may be used for attacks. The pull request to fix this issue awaits acceptance.",
"title": "CVE description"
}
],
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"text": "A vulnerability was found in vllm-project vllm up to 0.29.0. Affected by this issue is some unknown functionality of the file vllm/v1/sample/thinking_budget_state.py. The manipulation results in inefficient algorithmic complexity. It is possible to launch the attack remotely. The pull request to fix this issue awaits acceptance.",
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"text": "vLLM through 0.29.0 contains a memory corruption vulnerability in the Triton _bincount_kernel where prompt token IDs index the penalty prompt-presence bitset without bounds checking against vocabulary size. Attackers can submit multimodal audio requests with tokens equal to vocabulary size, causing out-of-bounds writes that corrupt concurrent requests\u0027 sampler state and alter repetition penalty behavior.",
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"category": "general",
"text": "vLLM through 0.29.0 fails to properly validate bad_words token indices against the model\u0027s generation output width in SamplingParams.update_from_tokenizer(). Attackers can supply out-of-bounds token indices that corrupt logits memory of concurrent requests, causing different in-flight HTTP requests to return incorrect tokens.",
"title": "CVE description"
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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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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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