PatchSiren cyber security CVE debrief
CVE-2026-109228 sgl-project CVE debrief
CVE-2026-109228 is a high-severity vulnerability in SGLang, a project that allows remote attackers to bypass resource budget limits via GLM-V video parameters. The vulnerability has a CVSS score of 8.7 and can be exploited by supplying specially crafted fps, max_frames, max_tokens_per_frame, and max_image_tokens values in the video_url object, potentially leading to a denial-of-service (DoS) attack by exhausting worker memory.
- Vendor
- sgl-project
- Product
- sglang
- CVSS
- HIGH 8.7
- CISA KEV
- Not listed in stored evidence
- Original CVE published
- 2026-10-12
- Original CVE updated
- 2026-10-12
- Advisory published
- 2026-10-12
- Advisory updated
- 2026-10-12
Who should care
Defenders responsible for SGLang deployments, especially those processing video content, should assess exposure and prioritize patching or mitigation. This includes operators managing SGLang instances, platform administrators, vulnerability management teams, and security teams responsible for monitoring and incident response.
Why it matters
CVE-2026-109228 is a high-severity vulnerability in SGLang that allows remote attackers to bypass resource budget limits via GLM-V video parameters, potentially leading to a denial-of-service (DoS) attack. Defenders should prioritize patching or mitigating this vulnerability, especially in environments where SGLang is used to process video content.
- Potential denial-of-service (DoS) attacks by exhausting worker memory
- Bypass of resource budget limits via GLM-V video parameters
- Possible impact on video processing services
- Need for validation of user-supplied video parameters
Technical summary
The vulnerability is caused by a lack of proper validation of user-supplied video parameters in SGLang, which allows attackers to override GLM-V video processing limits. This can lead to a DoS attack by exhausting worker memory. The vulnerability has a CVSS score of 8.7 and can be exploited by supplying specially crafted fps, max_frames, max_tokens_per_frame, and max_image_tokens values in the video_url object, potentially leading to a denial-of-service (DoS) attack by exhausting worker memory. Defenders should prioritize patching or mitigating this vulnerability, especially in environments where SGLang is used to process video content.
Defensive priority
Defenders should prioritize patching or mitigating this vulnerability, especially in environments where SGLang is used to process video content.
Recommended defensive actions
- Patch SGLang to version greater than 0.5.21
- Restrict access to SGLang to trusted users only
- Monitor SGLang logs for suspicious activity
- Implement rate limiting on video processing requests
- Review compensating controls for exposed systems while remediation is scheduled and verified
- Check relevant monitoring, detection, and logs for exposed assets that need extra review
- Track exceptions, retest remediated assets, and close the item only after evidence is documented.
Evidence notes
The vulnerability is caused by a lack of proper validation of user-supplied video parameters, which allows attackers to override GLM-V video processing limits. This can lead to a DoS attack by exhausting worker memory.
Sources and references
Verified primary and authoritative sources
-
CVE-2026-109228 CVE Program record
Publisher, destination, and source semantics verified
URL: https://www.cve.org/CVERecord?id=CVE-2026-109228
CVE Program - Official CVE Program record with source-provided CVE metadata.
-
CVE-2026-109228 NVD vulnerability detail
Publisher, destination, and source semantics verified
URL: https://nvd.nist.gov/vuln/detail/CVE-2026-109228
NIST National Vulnerability Database - Official NIST NVD detail page and source-specific vulnerability assessment.
Supplemental references
-
SGLang through 0.5.21 Resource Budget Bypass via GLM-V Video Parameters
Unverified legacy reference
URL: https://raw.githubusercontent.com/CVEProject/cvelistV5/main/cves/2026/109xxx/CVE-2026-109228.json
cve_program_cvelist_v5
-
Source reference
Unverified legacy reference
URL: https://github.com/sgl-project/sglang/issues/43705
Supplemental source - issue-tracking
-
Source reference
Unverified legacy reference
URL: https://github.com/sgl-project/sglang/blob/v0.5.21/python/sglang/srt/entrypoints/openai/protocol.py
Supplemental source - technical-description
-
Source reference
Unverified legacy reference
URL: https://github.com/sgl-project/sglang/blob/v0.5.21/python/sglang/srt/parser/conversation.py
Supplemental source - technical-description
-
Source reference
Unverified legacy reference
URL: https://github.com/sgl-project/sglang/blob/v0.5.21/python/sglang/srt/multimodal/processors/glm4v.py
Supplemental source - technical-description
-
Source reference
Unverified legacy reference
URL: https://github.com/sgl-project/sglang
Supplemental source - product
-
Source reference
Unverified legacy reference
URL: https://www.vulncheck.com/advisories/sglang-through-0.5.21-resource-budget-bypass-via-glm-v-video-parameters
Supplemental source - third-party-advisory
Methodology and review provenance
AI-assisted synthesis based on stored public vulnerability evidence. System validation, approval state, and publication status do not by themselves establish human review of this revision. PatchSiren helps prioritize defensive review and does not prove exposure or remediation on any system.