CVE-2026-34755

Ein Angreifer kann mehrere Schwachstellen in vllm ausnutzen, um Dateien zu manipulieren, Sicherheitsmaßnahmen zu umgehen, vertrauliche Informationen offenzulegen oder einen Denial-of-Service-Zustand herbeizuführen.

## 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 |

vLLM is an inference and serving engine for large language models (LLMs). From 0.7.0 to before 0.19.0, the VideoMediaIO.load_base64() method at vllm/multimodal/media/video.py 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, 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. This vulnerability is fixed in 0.19.0.