Low
osv
·
GHSA-pq5c-rjhq-qp7p
vLLM: Denial of Service via Unbounded Frame Count in video/jpeg Base64 Processing
Published Apr 3, 2026
CVSS 3.1
## 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
```python
# 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()` at `vllm/multimodal/utils.py:96-108` generates `data:video/jpeg;base64,...` URLs
- Official test suites at `tests/entrypoints/openai/test_video.py:62` and `tests/entrypoints/test_chat_utils.py:153` both 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 → OOM
```
`connector.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 |
Affected AI Products
vllm