### Summary A Server Side Request Forgery (SSRF) vulnerability in `download_bytes_from_url` allows any actor who can control batch input JSON to make the vLLM batch runner issue arbitrary HTTP/HTTPS requests from the server, without any URL validation or domain restrictions. This can be used to target internal services (e.g. cloud metadata endpoints or internal HTTP APIs) reachable from the vLLM host. ------ ### Details #### Vulnerable component The vulnerable logic is in the batch runner entrypoint `vllm/entrypoints/openai/run_batch.py`, function `download_bytes_from_url`: ``` # run_batch.py Lines 442-482 async def download_bytes_from_url(url: str) -> bytes: """ Download data from a URL or decode from a data URL. Args: url: Either an HTTP/HTTPS URL or a data URL (data:...;base64,...) Returns: Data as bytes """ parsed = urlparse(url) # Handle data URLs (base64 encoded) if parsed.scheme == "data": # Format: data:...;base64,<base64_data> if "," in url: header, data = url.split(",", 1) if "base64" in header: return base64.b64decode(data) else: raise ValueError(f"Unsupported data URL encoding: {header}") else: raise ValueError(f"Invalid data URL format: {url}") # Handle HTTP/HTTPS URLs elif parsed.scheme in ("http", "https"): async with ( aiohttp.ClientSession() as session, session.get(url) as resp, ): if resp.status != 200: raise Exception( f"Failed to download data from URL: {url}. Status: {resp.status}" ) return await resp.read() else: raise ValueError( f"Unsupported URL scheme: {parsed.scheme}. " "Supported schemes: http, https, data" ) ``` Key properties: - The function only parses the URL to dispatch on the scheme (`data`, `http`, `https`). - For `http` / `https`, it directly calls `session.get(url)` on the provided string. - There is no validation of: - hostname or IP address, - whether the target is internal or external, - port number, - path, query, or redirect target. - This is in contrast to the multimodal media path (`MediaConnector`), which implements an explicit domain allowlist. `download_bytes_from_url` does not reuse that protection. #### URL controllability The `url` argument is fully controlled by batch input JSON via the `file_url` field of `BatchTranscriptionRequest` / `BatchTranslationRequest`. 1. Batch request body type: ``` # run_batch.py Line 67-80 class BatchTranscriptionRequest(TranscriptionRequest): """ Batch transcription request that uses file_url instead of file. This class extends TranscriptionRequest but replaces the file field with file_url to support batch processing from audio files written in JSON format. """ file_url: str = Field( ..., description=( "Either a URL of the audio or a data URL with base64 encoded audio data. " ), ) ``` ``` # run_batch.py Line 98-111 class BatchTranslationRequest(TranslationRequest): """ Batch translation request that uses file_url instead of file. This class extends TranslationRequest but replaces the file field with file_url to support batch processing from audio files written in JSON format. """ file_url: str = Field( ..., description=( "Either a URL of the audio or a data URL with base64 encoded audio data. " ), ) ``` There is no restriction on the domain, IP, or port of `file_url` in these models. 1. Batch input is parsed directly from the batch file: ``` # run_batch.py Line 139-179 class BatchRequestInput(OpenAIBaseModel): ... url: str body: BatchRequestInputBody @field_validator("body", mode="plain") @classmethod def check_type_for_url(cls, value: Any, info: ValidationInfo): url: str = info.data["url"] ... if url == "/v1/audio/transcriptions": return BatchTranscriptionRequest.model_validate(value) if url == "/v1/audio/translations": return BatchTranslationRequest.model_validate(value) ``` ``` # run_batch.py Line 770-781 logger.info("Reading batch from %s...", args.input_file) # Submit all requests in the file to the engine "concurrently". response_futures: list[Awaitable[BatchRequestOutput]] = [] for request_json in (await read_file(args.input_file)).strip().split("\n"): # Skip empty lines. request_json = request_json.strip() if not request_json: continue request = BatchRequestInput.model_validate_json(request_json) ``` The batch runner reads each line of the input file (`args.input_file`), parses it as JSON, and constructs a `BatchTranscriptionRequest` / `BatchTranslationRequest`. Whatever `file_url` appears in that JSON line becomes `batch_request_body.file_url`. 1. `file_url` is passed directly into `download_bytes_from_url`: ``` # run_batch.py Line 610-623 def wrapper(handler_fn: Callable): async def transcription_wrapper( batch_request_body: (BatchTranscriptionRequest | BatchTranslationRequest), ) -> ( TranscriptionResponse | TranscriptionResponseVerbose | TranslationResponse | TranslationResponseVerbose | ErrorResponse ): try: # Download data from URL audio_data = await download_bytes_from_url(batch_request_body.file_url) ``` So the data flow is: 1. Attacker supplies JSON line in the batch input file with arbitrary `body.file_url`. 2. `BatchRequestInput` / `BatchTranscriptionRequest` / `BatchTranslationRequest` parse that JSON and store `file_url` verbatim. 3. `make_transcription_wrapper` calls `download_bytes_from_url(batch_request_body.file_url)`. 4. `download_bytes_from_url`’s HTTP/HTTPS branch issues `aiohttp.ClientSession().get(url)` to that attacker-controlled URL with no further validation. This is a classic SSRF pattern: a server-side component makes arbitrary HTTP requests to a URL string taken from untrusted input. #### Comparison with safer code The project already contains a safer URL-handling path for multimodal media in `vllm/multimodal/media/connector.py`, which demonstrates the intent to mitigate SSRF via domain allowlists and URL normalization: ``` # connector.py Lines 169-189 def load_from_url( self, url: str, media_io: MediaIO[_M], *, fetch_timeout: int | None = None, ) -> _M: # type: ignore[type-var] url_spec = parse_url(url) if url_spec.scheme and url_spec.scheme.startswith("http"): self._assert_url_in_allowed_media_domains(url_spec) connection = self.connection data = connection.get_bytes( url_spec.url, timeout=fetch_timeout, allow_redirects=envs.VLLM_MEDIA_URL_ALLOW_REDIRECTS, ) return media_io.load_bytes(data) ``` and: ``` # connector.py Lines 158-167 def _assert_url_in_allowed_media_domains(self, url_spec: Url) -> None: if ( self.allowed_media_domains and url_spec.hostname not in self.allowed_media_domains ): raise ValueError( f"The URL must be from one of the allowed domains: " f"{self.allowed_media_domains}. Input URL domain: " f"{url_spec.hostname}" ) ``` `download_bytes_from_url` does not reuse this allowlist or any equivalent validation, even though it also fetches user-provided URLs.
### Summary A Server Side Request Forgery (SSRF) vulnerability in `download_bytes_from_url` allows any actor who can control batch input JSON to make the vLLM batch runner issue arbitrary HTTP/HTTPS requests from the server, without any URL validation or domain restrictions. This can be used to target internal services (e.g. cloud metadata endpoints or internal HTTP APIs) reachable from the vLLM host. ------ ### Details #### Vulnerable component The vulnerable logic is in the batch runner entrypoint `vllm/entrypoints/openai/run_batch.py`, function `download_bytes_from_url`: ``` # run_batch.py Lines 442-482 async def download_bytes_from_url(url: str) -> bytes: """ Download data from a URL or decode from a data URL. Args: url: Either an HTTP/HTTPS URL or a data URL (data:...;base64,...) Returns: Data as bytes """ parsed = urlparse(url) # Handle data URLs (base64 encoded) if parsed.scheme == "data": # Format: data:...;base64,<base64_data> if "," in url: header, data = url.split(",", 1) if "base64" in header: return base64.b64decode(data) else: raise ValueError(f"Unsupported data URL encoding: {header}") else: raise ValueError(f"Invalid data URL format: {url}") # Handle HTTP/HTTPS URLs elif parsed.scheme in ("http", "https"): async with ( aiohttp.ClientSession() as session, session.get(url) as resp, ): if resp.status != 200: raise Exception( f"Failed to download data from URL: {url}. Status: {resp.status}" ) return await resp.read() else: raise ValueError( f"Unsupported URL scheme: {parsed.scheme}. " "Supported schemes: http, https, data" ) ``` Key properties: - The function only parses the URL to dispatch on the scheme (`data`, `http`, `https`). - For `http` / `https`, it directly calls `session.get(url)` on the provided string. - There is no validation of: - hostname or IP address, - whether the target is internal or external, - port number, - path, query, or redirect target. - This is in contrast to the multimodal media path (`MediaConnector`), which implements an explicit domain allowlist. `download_bytes_from_url` does not reuse that protection. #### URL controllability The `url` argument is fully controlled by batch input JSON via the `file_url` field of `BatchTranscriptionRequest` / `BatchTranslationRequest`. 1. Batch request body type: ``` # run_batch.py Line 67-80 class BatchTranscriptionRequest(TranscriptionRequest): """ Batch transcription request that uses file_url instead of file. This class extends TranscriptionRequest but replaces the file field with file_url to support batch processing from audio files written in JSON format. """ file_url: str = Field( ..., description=( "Either a URL of the audio or a data URL with base64 encoded audio data. " ), ) ``` ``` # run_batch.py Line 98-111 class BatchTranslationRequest(TranslationRequest): """ Batch translation request that uses file_url instead of file. This class extends TranslationRequest but replaces the file field with file_url to support batch processing from audio files written in JSON format. """ file_url: str = Field( ..., description=( "Either a URL of the audio or a data URL with base64 encoded audio data. " ), ) ``` There is no restriction on the domain, IP, or port of `file_url` in these models. 1. Batch input is parsed directly from the batch file: ``` # run_batch.py Line 139-179 class BatchRequestInput(OpenAIBaseModel): ... url: str body: BatchRequestInputBody @field_validator("body", mode="plain") @classmethod def check_type_for_url(cls, value: Any, info: ValidationInfo): url: str = info.data["url"] ... if url == "/v1/audio/transcriptions": return BatchTranscriptionRequest.model_validate(value) if url == "/v1/audio/translations": return BatchTranslationRequest.model_validate(value) ``` ``` # run_batch.py Line 770-781 logger.info("Reading batch from %s...", args.input_file) # Submit all requests in the file to the engine "concurrently". response_futures: list[Awaitable[BatchRequestOutput]] = [] for request_json in (await read_file(args.input_file)).strip().split("\n"): # Skip empty lines. request_json = request_json.strip() if not request_json: continue request = BatchRequestInput.model_validate_json(request_json) ``` The batch runner reads each line of the input file (`args.input_file`), parses it as JSON, and constructs a `BatchTranscriptionRequest` / `BatchTranslationRequest`. Whatever `file_url` appears in that JSON line becomes `batch_request_body.file_url`. 1. `file_url` is passed directly into `download_bytes_from_url`: ``` # run_batch.py Line 610-623 def wrapper(handler_fn: Callable): async def transcription_wrapper( batch_request_body: (BatchTranscriptionRequest | BatchTranslationRequest), ) -> ( TranscriptionResponse | TranscriptionResponseVerbose | TranslationResponse | TranslationResponseVerbose | ErrorResponse ): try: # Download data from URL audio_data = await download_bytes_from_url(batch_request_body.file_url) ``` So the data flow is: 1. Attacker supplies JSON line in the batch input file with arbitrary `body.file_url`. 2. `BatchRequestInput` / `BatchTranscriptionRequest` / `BatchTranslationRequest` parse that JSON and store `file_url` verbatim. 3. `make_transcription_wrapper` calls `download_bytes_from_url(batch_request_body.file_url)`. 4. `download_bytes_from_url`’s HTTP/HTTPS branch issues `aiohttp.ClientSession().get(url)` to that attacker-controlled URL with no further validation. This is a classic SSRF pattern: a server-side component makes arbitrary HTTP requests to a URL string taken from untrusted input. #### Comparison with safer code The project already contains a safer URL-handling path for multimodal media in `vllm/multimodal/media/connector.py`, which demonstrates the intent to mitigate SSRF via domain allowlists and URL normalization: ``` # connector.py Lines 169-189 def load_from_url( self, url: str, media_io: MediaIO[_M], *, fetch_timeout: int | None = None, ) -> _M: # type: ignore[type-var] url_spec = parse_url(url) if url_spec.scheme and url_spec.scheme.startswith("http"): self._assert_url_in_allowed_media_domains(url_spec) connection = self.connection data = connection.get_bytes( url_spec.url, timeout=fetch_timeout, allow_redirects=envs.VLLM_MEDIA_URL_ALLOW_REDIRECTS, ) return media_io.load_bytes(data) ``` and: ``` # connector.py Lines 158-167 def _assert_url_in_allowed_media_domains(self, url_spec: Url) -> None: if ( self.allowed_media_domains and url_spec.hostname not in self.allowed_media_domains ): raise ValueError( f"The URL must be from one of the allowed domains: " f"{self.allowed_media_domains}. Input URL domain: " f"{url_spec.hostname}" ) ``` `download_bytes_from_url` does not reuse this allowlist or any equivalent validation, even though it also fetches user-provided URLs.
Update vllm to 0.19.0 if you use the affected versions. Test the change in a non-production environment first.
Local check
hol-guard supply-chain scanvLLM: Server-Side Request Forgery (SSRF) in `download_bytes_from_url ` affects vllm (pip). Severity is medium. ### Summary A Server Side Request Forgery (SSRF) vulnerability in `download_bytes_from_url` allows any actor who can control batch input JSON to make the vLLM batch runner issue arbitrary HTTP/HTTPS requests from the server, without any URL validation or domain restrictions. This can be used to target internal services (e.g. cloud metadata endpoints or internal HTTP APIs) reachable from the vLLM host. ------ ### Details #### Vulnerable component The vulnerable logic is in the batch runner entrypoint `vllm/entrypoints/openai/run_batch.py`, function `download_bytes_from_url`: ``` # run_batch.py Lines 442-482 async def download_bytes_from_url(url: str) -> bytes: """ Download data from a URL or decode from a data URL. Args: url: Either an HTTP/HTTPS URL or a data URL (data:...;base64,...) Returns: Data as bytes """ parsed = urlparse(url) # Handle data URLs (base64 encoded) if parsed.scheme == "data": # Format: data:...;base64,<base64_data> if "," in url: header, data = url.split(",", 1) if "base64" in header: return base64.b64decode(data) else: raise ValueError(f"Unsupported data URL encoding: {header}") else: raise ValueError(f"Invalid data URL format: {url}") # Handle HTTP/HTTPS URLs elif parsed.scheme in ("http", "https"): async with ( aiohttp.ClientSession() as session, session.get(url) as resp, ): if resp.status != 200: raise Exception( f"Failed to download data from URL: {url}. Status: {resp.status}" ) return await resp.read() else: raise ValueError( f"Unsupported URL scheme: {parsed.scheme}. " "Supported schemes: http, https, data" ) ``` Key properties: - The function only parses the URL to dispatch on the scheme (`data`, `http`, `https`). - For `http` / `https`, it directly calls `session.get(url)` on the provided string. - There is no validation of: - hostname or IP address, - whether the target is internal or external, - port number, - path, query, or redirect target. - This is in contrast to the multimodal media path (`MediaConnector`), which implements an explicit domain allowlist. `download_bytes_from_url` does not reuse that protection. #### URL controllability The `url` argument is fully controlled by batch input JSON via the `file_url` field of `BatchTranscriptionRequest` / `BatchTranslationRequest`. 1. Batch request body type: ``` # run_batch.py Line 67-80 class BatchTranscriptionRequest(TranscriptionRequest): """ Batch transcription request that uses file_url instead of file. This class extends TranscriptionRequest but replaces the file field with file_url to support batch processing from audio files written in JSON format. """ file_url: str = Field( ..., description=( "Either a URL of the audio or a data URL with base64 encoded audio data. " ), ) ``` ``` # run_batch.py Line 98-111 class BatchTranslationRequest(TranslationRequest): """ Batch translation request that uses file_url instead of file. This class extends TranslationRequest but replaces the file field with file_url to support batch processing from audio files written in JSON format. """ file_url: str = Field( ..., description=( "Either a URL of the audio or a data URL with base64 encoded audio data. " ), ) ``` There is no restriction on the domain, IP, or port of `file_url` in these models. 1. Batch input is parsed directly from the batch file: ``` # run_batch.py Line 139-179 class BatchRequestInput(OpenAIBaseModel): ... url: str body: BatchRequestInputBody @field_validator("body", mode="plain") @classmethod def check_type_for_url(cls, value: Any, info: ValidationInfo): url: str = info.data["url"] ... if url == "/v1/audio/transcriptions": return BatchTranscriptionRequest.model_validate(value) if url == "/v1/audio/translations": return BatchTranslationRequest.model_validate(value) ``` ``` # run_batch.py Line 770-781 logger.info("Reading batch from %s...", args.input_file) # Submit all requests in the file to the engine "concurrently". response_futures: list[Awaitable[BatchRequestOutput]] = [] for request_json in (await read_file(args.input_file)).strip().split("\n"): # Skip empty lines. request_json = request_json.strip() if not request_json: continue request = BatchRequestInput.model_validate_json(request_json) ``` The batch runner reads each line of the input file (`args.input_file`), parses it as JSON, and constructs a `BatchTranscriptionRequest` / `BatchTranslationRequest`. Whatever `file_url` appears in that JSON line becomes `batch_request_body.file_url`. 1. `file_url` is passed directly into `download_bytes_from_url`: ``` # run_batch.py Line 610-623 def wrapper(handler_fn: Callable): async def transcription_wrapper( batch_request_body: (BatchTranscriptionRequest | BatchTranslationRequest), ) -> ( TranscriptionResponse | TranscriptionResponseVerbose | TranslationResponse | TranslationResponseVerbose | ErrorResponse ): try: # Download data from URL audio_data = await download_bytes_from_url(batch_request_body.file_url) ``` So the data flow is: 1. Attacker supplies JSON line in the batch input file with arbitrary `body.file_url`. 2. `BatchRequestInput` / `BatchTranscriptionRequest` / `BatchTranslationRequest` parse that JSON and store `file_url` verbatim. 3. `make_transcription_wrapper` calls `download_bytes_from_url(batch_request_body.file_url)`. 4. `download_bytes_from_url`’s HTTP/HTTPS branch issues `aiohttp.ClientSession().get(url)` to that attacker-controlled URL with no further validation. This is a classic SSRF pattern: a server-side component makes arbitrary HTTP requests to a URL string taken from untrusted input. #### Comparison with safer code The project already contains a safer URL-handling path for multimodal media in `vllm/multimodal/media/connector.py`, which demonstrates the intent to mitigate SSRF via domain allowlists and URL normalization: ``` # connector.py Lines 169-189 def load_from_url( self, url: str, media_io: MediaIO[_M], *, fetch_timeout: int | None = None, ) -> _M: # type: ignore[type-var] url_spec = parse_url(url) if url_spec.scheme and url_spec.scheme.startswith("http"): self._assert_url_in_allowed_media_domains(url_spec) connection = self.connection data = connection.get_bytes( url_spec.url, timeout=fetch_timeout, allow_redirects=envs.VLLM_MEDIA_URL_ALLOW_REDIRECTS, ) return media_io.load_bytes(data) ``` and: ``` # connector.py Lines 158-167 def _assert_url_in_allowed_media_domains(self, url_spec: Url) -> None: if ( self.allowed_media_domains and url_spec.hostname not in self.allowed_media_domains ): raise ValueError( f"The URL must be from one of the allowed domains: " f"{self.allowed_media_domains}. Input URL domain: " f"{url_spec.hostname}" ) ``` `download_bytes_from_url` does not reuse this allowlist or any equivalent validation, even though it also fetches user-provided URLs.
AI coding agents often install or upgrade packages automatically in pip. A medium vulnerability in a dependency can be pulled into a project through a normal install or update without a human reviewing the change, expanding the blast radius from a single package to every agent workspace that depends on it.
| Package | Affected range | Fixed version |
|---|---|---|
| vllmpip | >=0.16.0,<0.19.0 | 0.19.0 |
Fixed versions are reported by the source feed; confirm compatibility before updating.
Reported by GitHub Security Advisories (ghsa).
HOL Guard can help your team review package activity against supported protection paths.
Explore HOL GuardUpdate vllm to 0.19.0 if you use the affected versions. Test the change in a non-production environment first.
Local check
hol-guard supply-chain scanvLLM: Server-Side Request Forgery (SSRF) in `download_bytes_from_url ` affects vllm (pip). Severity is medium. ### Summary A Server Side Request Forgery (SSRF) vulnerability in `download_bytes_from_url` allows any actor who can control batch input JSON to make the vLLM batch runner issue arbitrary HTTP/HTTPS requests from the server, without any URL validation or domain restrictions. This can be used to target internal services (e.g. cloud metadata endpoints or internal HTTP APIs) reachable from the vLLM host. ------ ### Details #### Vulnerable component The vulnerable logic is in the batch runner entrypoint `vllm/entrypoints/openai/run_batch.py`, function `download_bytes_from_url`: ``` # run_batch.py Lines 442-482 async def download_bytes_from_url(url: str) -> bytes: """ Download data from a URL or decode from a data URL. Args: url: Either an HTTP/HTTPS URL or a data URL (data:...;base64,...) Returns: Data as bytes """ parsed = urlparse(url) # Handle data URLs (base64 encoded) if parsed.scheme == "data": # Format: data:...;base64,<base64_data> if "," in url: header, data = url.split(",", 1) if "base64" in header: return base64.b64decode(data) else: raise ValueError(f"Unsupported data URL encoding: {header}") else: raise ValueError(f"Invalid data URL format: {url}") # Handle HTTP/HTTPS URLs elif parsed.scheme in ("http", "https"): async with ( aiohttp.ClientSession() as session, session.get(url) as resp, ): if resp.status != 200: raise Exception( f"Failed to download data from URL: {url}. Status: {resp.status}" ) return await resp.read() else: raise ValueError( f"Unsupported URL scheme: {parsed.scheme}. " "Supported schemes: http, https, data" ) ``` Key properties: - The function only parses the URL to dispatch on the scheme (`data`, `http`, `https`). - For `http` / `https`, it directly calls `session.get(url)` on the provided string. - There is no validation of: - hostname or IP address, - whether the target is internal or external, - port number, - path, query, or redirect target. - This is in contrast to the multimodal media path (`MediaConnector`), which implements an explicit domain allowlist. `download_bytes_from_url` does not reuse that protection. #### URL controllability The `url` argument is fully controlled by batch input JSON via the `file_url` field of `BatchTranscriptionRequest` / `BatchTranslationRequest`. 1. Batch request body type: ``` # run_batch.py Line 67-80 class BatchTranscriptionRequest(TranscriptionRequest): """ Batch transcription request that uses file_url instead of file. This class extends TranscriptionRequest but replaces the file field with file_url to support batch processing from audio files written in JSON format. """ file_url: str = Field( ..., description=( "Either a URL of the audio or a data URL with base64 encoded audio data. " ), ) ``` ``` # run_batch.py Line 98-111 class BatchTranslationRequest(TranslationRequest): """ Batch translation request that uses file_url instead of file. This class extends TranslationRequest but replaces the file field with file_url to support batch processing from audio files written in JSON format. """ file_url: str = Field( ..., description=( "Either a URL of the audio or a data URL with base64 encoded audio data. " ), ) ``` There is no restriction on the domain, IP, or port of `file_url` in these models. 1. Batch input is parsed directly from the batch file: ``` # run_batch.py Line 139-179 class BatchRequestInput(OpenAIBaseModel): ... url: str body: BatchRequestInputBody @field_validator("body", mode="plain") @classmethod def check_type_for_url(cls, value: Any, info: ValidationInfo): url: str = info.data["url"] ... if url == "/v1/audio/transcriptions": return BatchTranscriptionRequest.model_validate(value) if url == "/v1/audio/translations": return BatchTranslationRequest.model_validate(value) ``` ``` # run_batch.py Line 770-781 logger.info("Reading batch from %s...", args.input_file) # Submit all requests in the file to the engine "concurrently". response_futures: list[Awaitable[BatchRequestOutput]] = [] for request_json in (await read_file(args.input_file)).strip().split("\n"): # Skip empty lines. request_json = request_json.strip() if not request_json: continue request = BatchRequestInput.model_validate_json(request_json) ``` The batch runner reads each line of the input file (`args.input_file`), parses it as JSON, and constructs a `BatchTranscriptionRequest` / `BatchTranslationRequest`. Whatever `file_url` appears in that JSON line becomes `batch_request_body.file_url`. 1. `file_url` is passed directly into `download_bytes_from_url`: ``` # run_batch.py Line 610-623 def wrapper(handler_fn: Callable): async def transcription_wrapper( batch_request_body: (BatchTranscriptionRequest | BatchTranslationRequest), ) -> ( TranscriptionResponse | TranscriptionResponseVerbose | TranslationResponse | TranslationResponseVerbose | ErrorResponse ): try: # Download data from URL audio_data = await download_bytes_from_url(batch_request_body.file_url) ``` So the data flow is: 1. Attacker supplies JSON line in the batch input file with arbitrary `body.file_url`. 2. `BatchRequestInput` / `BatchTranscriptionRequest` / `BatchTranslationRequest` parse that JSON and store `file_url` verbatim. 3. `make_transcription_wrapper` calls `download_bytes_from_url(batch_request_body.file_url)`. 4. `download_bytes_from_url`’s HTTP/HTTPS branch issues `aiohttp.ClientSession().get(url)` to that attacker-controlled URL with no further validation. This is a classic SSRF pattern: a server-side component makes arbitrary HTTP requests to a URL string taken from untrusted input. #### Comparison with safer code The project already contains a safer URL-handling path for multimodal media in `vllm/multimodal/media/connector.py`, which demonstrates the intent to mitigate SSRF via domain allowlists and URL normalization: ``` # connector.py Lines 169-189 def load_from_url( self, url: str, media_io: MediaIO[_M], *, fetch_timeout: int | None = None, ) -> _M: # type: ignore[type-var] url_spec = parse_url(url) if url_spec.scheme and url_spec.scheme.startswith("http"): self._assert_url_in_allowed_media_domains(url_spec) connection = self.connection data = connection.get_bytes( url_spec.url, timeout=fetch_timeout, allow_redirects=envs.VLLM_MEDIA_URL_ALLOW_REDIRECTS, ) return media_io.load_bytes(data) ``` and: ``` # connector.py Lines 158-167 def _assert_url_in_allowed_media_domains(self, url_spec: Url) -> None: if ( self.allowed_media_domains and url_spec.hostname not in self.allowed_media_domains ): raise ValueError( f"The URL must be from one of the allowed domains: " f"{self.allowed_media_domains}. Input URL domain: " f"{url_spec.hostname}" ) ``` `download_bytes_from_url` does not reuse this allowlist or any equivalent validation, even though it also fetches user-provided URLs.
AI coding agents often install or upgrade packages automatically in pip. A medium vulnerability in a dependency can be pulled into a project through a normal install or update without a human reviewing the change, expanding the blast radius from a single package to every agent workspace that depends on it.
| Package | Affected range | Fixed version |
|---|---|---|
| vllmpip | >=0.16.0,<0.19.0 | 0.19.0 |
Fixed versions are reported by the source feed; confirm compatibility before updating.
Reported by GitHub Security Advisories (ghsa).
HOL Guard can help your team review package activity against supported protection paths.
Explore HOL Guard