Severity by source
AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Primary rating from Vendor (https://github.com/vllm-project/vllm).
CVSS VectorVendor: https://github.com/vllm-project/vllm
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Lifecycle Timeline
4Blast Radius
ecosystem impact- 3 pypi packages depend on vllm (3 direct, 0 indirect)
Ecosystem-wide dependent count for version 0.7.0.
DescriptionCVE.org
Summary
The VideoMediaIO.load_base64() method at vllm/multimodal/media/video.py:51-62 splits video/jpeg data URLs by comma to extract individual JPEG frames, but does not enforce a frame count limit. The num_frames parameter (default: 32), which is enforced by the load_bytes() code path at line 47-48, is completely bypassed in the video/jpeg base64 path. An attacker can send a single API request containing thousands of comma-separated base64-encoded JPEG frames, causing the server to decode all frames into memory and crash with OOM.
Details
Vulnerable code
# video.py:51-62
def load_base64(self, media_type: str, data: str) -> tuple[npt.NDArray, dict[str, Any]]:
if media_type.lower() == "video/jpeg":
load_frame = partial(self.image_io.load_base64, "image/jpeg")
return np.stack(
[np.asarray(load_frame(frame_data)) for frame_data in data.split(",")]
# ^^^^^^^^^^
# Unbounded split - no frame count limit
), {}
return self.load_bytes(base64.b64decode(data))The load_bytes() path (line 47-48) properly delegates to a video loader that respects self.num_frames (default 32). The load_base64("video/jpeg", ...) path bypasses this limit entirely - data.split(",") produces an unbounded list and every frame is decoded into a numpy array.
video/jpeg is part of vLLM's public API
video/jpeg is a vLLM-specific MIME type, not IANA-registered. However it is part of the public API surface:
encode_video_url()atvllm/multimodal/utils.py:96-108generatesdata:video/jpeg;base64,...URLs- Official test suites at
tests/entrypoints/openai/test_video.py:62andtests/entrypoints/test_chat_utils.py:153both use this format
Memory amplification
Each JPEG frame decodes to a full numpy array. For 640x480 RGB images, each frame is ~921 KB decoded. 5000 frames = ~4.6 GB. np.stack() then creates an additional copy. The compressed JPEG payload is small (~100 KB for 5000 frames) but decompresses to gigabytes.
Data flow
POST /v1/chat/completions
→ chat_utils.py:1434 video_url type → mm_parser.parse_video()
→ chat_utils.py:872 parse_video() → self._connector.fetch_video()
→ connector.py:295 fetch_video() → load_from_url(url, self.video_io)
→ connector.py:91 _load_data_url(): url_spec.path.split(",", 1)
→ media_type = "video/jpeg"
→ data = "<frame1>,<frame2>,...,<frame10000>"
→ connector.py:100 media_io.load_base64("video/jpeg", data)
→ video.py:54 data.split(",") ← UNBOUNDED
→ video.py:55-57 all frames decoded into numpy arrays
→ video.py:56 np.stack([...]) ← massive combined array → OOMconnector.py:91 uses split(",", 1) which splits on only the first comma. All remaining commas stay in data and are later split by video.py:54.
Comparison with existing protections
| Code Path | Frame Limit | File |
|---|---|---|
load_bytes() (binary video) | Yes - num_frames (default 32) | video.py:46-49 |
load_base64("video/jpeg", ...) | No - unlimited data.split(",") | video.py:51-62 |
AnalysisAI
Denial of service in vLLM's VideoMediaIO.load_base64() method allows authenticated remote attackers to crash the server via memory exhaustion by sending API requests with thousands of comma-separated base64-encoded JPEG frames. The vulnerability bypasses the default 32-frame limit enforced in other video loading code paths, allowing attackers to decode gigabytes of image data into memory (e.g., 5000 frames ≈ 4.6 GB for 640x480 RGB) with a small compressed payload. CVSS 6.5 (network-accessible, low complexity, requires authentication, high availability impact); no public exploit code identified at time of analysis.
Technical ContextAI
vLLM is a Python-based LLM inference framework (CPE: pkg:pip/vllm) that provides multimodal input support including video processing via its VideoMediaIO class. The vulnerability stems from improper input validation in the load_base64() method at vllm/multimodal/media/video.py, lines 51-62. When processing video/jpeg MIME type (a vLLM-specific format, not IANA-registered), the code uses Python's str.split(",") to extract individual base64-encoded JPEG frames without enforcing any frame count limit. In contrast, the load_bytes() code path for binary video input properly respects self.num_frames (default 32). The root cause is CWE-770 (Allocation of Resources Without Limits or Throttling): the unbounded list comprehension [np.asarray(load_frame(frame_data)) for frame_data in data.split(",")] followed by np.stack() creates memory amplification. Each JPEG frame decompresses to ~921 KB for 640x480 RGB, and np.stack() allocates an additional contiguous copy, making small compressed payloads decompress to gigabytes.
RemediationAI
Patch the vulnerability by applying the fix from GitHub commit 58ee61422169ce17e08248f8efa1e9df434fe395 or PR #38636, which enforces the frame count limit in the load_base64() video/jpeg code path matching the behavior of load_bytes(). Update vLLM to the patched version (exact version number not specified in input but available from https://github.com/vllm-project/vllm/pull/38636). As a temporary mitigation before patching, restrict or disable video/jpeg input support via API configuration, implement strict HTTP request size limits (e.g., Content-Length headers), or deploy request size validation upstream of vLLM. Review and test the vendor's patch thoroughly in staging before production deployment. Refer to the GitHub Security Advisory at https://github.com/advisories/GHSA-pq5c-rjhq-qp7p for detailed remediation guidance.
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External POC / Exploit Code
Leaving vuln.today
EUVD-2026-19350
GHSA-pq5c-rjhq-qp7p