GHSA-v82g-2437-67m2 CVE-2026-55646 PYSEC-2026-2305
vLLM: Speech-to-text upload size limit is enforced after full UploadFile read
- Severity
- 6.5 / 10
- Medium · CVSS 3.1
- Exploit chance (EPSS)
- 0.52%
- Chance of exploitation in the next 30 days. Higher than 44.14% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-400 Uncontrolled Resource Consumption
- CWE-770 Allocation of Resources Without Limits or Throttling
- Fix
0.24.0- Upgrade vllm to this version or later
Description
Summary
Current-head vLLM documents VLLM_MAX_AUDIO_CLIP_FILESIZE_MB as the maximum audio file size accepted by the speech-to-text APIs. The default is 25 MB. vllm/envs.py also describes files larger than this value as rejected.
The /v1/audio/transcriptions and /v1/audio/translations routes call await request.file.read() before vLLM checks that limit. In FastAPI and Starlette, UploadFile.read() returns bytes from the uploaded file object; when called without a size argument, the route materializes the remaining file contents. vLLM then performs the compressed file-size check later in _preprocess_speech_to_text() against the already-created bytes object.
As a result, the documented compressed audio file-size limit does not bound the memory allocated by vLLM endpoint code before validation. An oversized multipart upload can cause vLLM to allocate memory proportional to the uploaded file size before rejecting the request as too large.
This is distinct from GHSA-6pr9-rp53-2pmc, which covered decoded PCM expansion after compressed input was accepted. This report covers compressed upload materialization before compressed-size validation.
Technical Details
The upload routes perform an unbounded read before vLLM checks the documented compressed audio file-size limit:
vllm/entrypoints/speech_to_text/transcription/api_router.py:audio_data = await request.file.read()vllm/entrypoints/speech_to_text/translation/api_router.py:audio_data = await request.file.read()vllm/entrypoints/speech_to_text/base/serving.pylater checks:if len(audio_data) / 1024**2 > self.max_audio_filesize_mb
There is no route-level check of request.file.size, Content-Length, a bounded read(max_bytes + 1), or a streaming copy that stops at the configured limit before the full file is materialized.
This does not appear to be intended behavior. vLLM's security guide treats request-controlled resource use as a security boundary: for example, requests that exceed VLLM_MAX_N_SEQUENCES are rejected before reaching the engine. The speech-to-text upload limit is documented in the same spirit as an API enforced limit, but the first vLLM check happens after the over-limit upload has already been copied into a bytes object.
Impact
Attack requirements:
- the deployment exposes
/v1/audio/transcriptionsor/v1/audio/translations; - a speech-to-text capable model/task is configured; and
- the caller can submit requests to the endpoint, including any API key the deployment requires.
An API caller who meets those requirements can send an oversized audio file. vLLM reads the full uploaded file into memory before applying the configured compressed audio file-size limit. This can create memory pressure or, depending on process/container limits and concurrency, terminate the process before the request is rejected.
Suggested severity: Moderate, CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H (6.5), CWE-770/CWE-400.
The impact is availability-only. This is not a claim of code execution, data access, cross-tenant data exposure, parser-level multipart memory exhaustion, or persistence after process restart. Deployment body-size limits at a reverse proxy or ASGI layer can mitigate the issue before vLLM sees the request, but vLLM's own documented file-size limit does not currently provide that memory boundary.
Suggested Fix
Enforce the compressed audio upload limit before the unbounded read:
- Check reliable upload size metadata before reading when available.
- Read at most
max_bytes + 1bytes in chunks as a defense-in-depth guard against missing or unreliable metadata. - Share the helper between transcription and translation routes.
- Add regression tests that prove an over-limit
UploadFileis rejected without calling an unboundedread().
The important property is that over-limit compressed uploads are rejected before vLLM allocates the full uploaded file as bytes.
Resources
- vLLM security policy:
https://github.com/vllm-project/vllm/security/policy - vLLM speech-to-text docs:
https://docs.vllm.ai/en/latest/serving/online_serving/speech_to_text/ - vLLM security guide, request parameter resource limits:
https://docs.vllm.ai/en/latest/usage/security/ - vLLM vulnerability management docs:
https://docs.vllm.ai/en/latest/contributing/vulnerability_management/ - FastAPI file uploads:
https://fastapi.tiangolo.com/reference/uploadfile/ - Starlette uploaded files:
https://www.starlette.io/requests/ - Adjacent published audio advisory:
https://github.com/vllm-project/vllm/security/advisories/GHSA-6pr9-rp53-2pmc - Request-parameter resource DoS precedent:
https://github.com/vllm-project/vllm/security/advisories/GHSA-3mwp-wvh9-7528
Appendix: Affected Version
Validated against current head:
- commit:
1033ffac2eccf986fdd880f4dee64ca3b22c63c9 - described version:
v0.22.1rc0-491-g1033ffac2e
Known affected range: current head. It has not been determined the introducing commit or release range.
Appendix: Proof Of Vulnerability
The attached proof is a non-destructive static probe. It does not upload a large file or contact a running vLLM server:
python3 attached-evidence/poc/audio_upload_size_precheck_probe.py
Observed result:
{
"pov": "this report",
"validated": true,
"default_limit_mb": 25,
"documented_api_limit": true,
"routes": {
"transcription_route": {
"unbounded_upload_read": true,
"early_size_guard_before_read": false,
"chunked_bounded_read": false
},
"translation_route": {
"unbounded_upload_read": true,
"early_size_guard_before_read": false,
"chunked_bounded_read": false
}
},
"late_size_check": {
"present": true
}
}
Expected behavior: vLLM rejects over-limit audio files before materializing the entire upload into memory in vLLM endpoint code.
Actual behavior: the route materializes the upload into memory first, and only then does vLLM reject the request as exceeding VLLM_MAX_AUDIO_CLIP_FILESIZE_MB.
Affected versions
PyPI
| Package | Vulnerable | Fixed in |
|---|---|---|
| vllm | >= 0.22.0, < 0.24.0 |
0.24.0 |
How to fix
Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.
vllm PyPI → 0.24.0
- pip
pip install --upgrade "vllm>=0.24.0"- poetry
poetry add "vllm>=0.24.0"- uv
uv add "vllm>=0.24.0"
Severity breakdown
CVSS 3.1 6.5
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- Low
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- None
- Integrity
- None
- Availability
- High
Weakness
CWE-400: Uncontrolled Resource Consumption
The product does not properly control the allocation and maintenance of a limited resource.
How to prevent it
- Architecture and Design: Design throttling mechanisms into the system architecture. The best protection is to limit the amount of resources that an unauthorized user can cause to be expended. A strong authentication and access control model will help prevent such attacks from occurring in the first place. The login application should be protected against DoS attacks as much as possible. Limiting the database access, perhaps by caching result sets, can help minimize the resources expended. To further limit the potential for a DoS attack, consider tracking the rate of requests received from users and blocking requests that exceed a defined rate threshold.
- Architecture and Design: Mitigation of resource exhaustion attacks requires that the target system either: recognizes the attack and denies that user further access for a given amount of time, or uniformly throttles all requests in order to make it more difficult to consume resources more quickly than they can again be freed. The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question. The second solution is simply difficult to effectively institute -- and even when properly done, it does not provide a full solution. It simply makes the attack require more resources on the part of the attacker.
- Architecture and Design: Ensure that protocols have specific limits of scale placed on them.
- Implementation: Ensure that all failures in resource allocation place the system into a safe posture.
CWE-770: Allocation of Resources Without Limits or Throttling
The product allocates a reusable resource or group of resources on behalf of an actor without imposing any intended restrictions on the size or number of resources that can be allocated.
How to prevent it
- Requirements: Clearly specify the minimum and maximum expectations for capabilities, and dictate which behaviors are acceptable when resource allocation reaches limits.
- Architecture and Design: Limit the amount of resources that are accessible to unprivileged users. Set per-user limits for resources. Allow the system administrator to define these limits. Be careful to avoid CWE-410.
- Architecture and Design: Design throttling mechanisms into the system architecture. The best protection is to limit the amount of resources that an unauthorized user can cause to be expended. A strong authentication and access control model will help prevent such attacks from occurring in the first place, and it will help the administrator to identify who is committing the abuse. The login application should be protected against DoS attacks as much as possible. Limiting the database access, perhaps by caching result sets, can help minimize the resources expended. To further limit the potential for a DoS attack, consider tracking the rate of requests received from users and blocking requests that exceed a defined rate threshold.
- Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
- Architecture and Design: For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server.
- Architecture and Design: Mitigation of resource exhaustion attacks requires that the target system either: recognizes the attack and denies that user further access for a given amount of time, typically by using increasing time delays uniformly throttles all requests in order to make it more difficult to consume resources more quickly than they can again be freed. The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question. The second solution can be difficult to effectively institute -- and even when properly done, it does not provide a full solution. It simply requires more resources on the part of the attacker.
- Architecture and Design: Ensure that protocols have specific limits of scale placed on them.
- Architecture and Design, Implementation: If the program must fail, ensure that it fails gracefully (fails closed). There may be a temptation to simply let the program fail poorly in cases such as low memory conditions, but an attacker may be able to assert control before the software has fully exited. Alternately, an uncontrolled failure could cause cascading problems with other downstream components; for example, the program could send a signal to a downstream process so the process immediately knows that a problem has occurred and has a better chance of recovery. Ensure that all failures in resource allocation place the system into a safe posture.
- Operation, Architecture and Design: Use quotas or other resource-limiting settings provided by the operating system or environment. For example, when managing system resources in POSIX, setrlimit() can be used to set limits for certain types of resources, and getrlimit() can determine how many resources are available. However, these functions are not available on all operating systems. When the current levels get close to the maximum that is defined for the application (see CWE-770), then limit the allocation of further resources to privileged users; alternately, begin releasing resources for less-privileged users. While this mitigation may protect the system from attack, it will not necessarily stop attackers from adversely impacting other users. Ensure that the application performs the appropriate error checks and error handling in case resources become unavailable (CWE-703).
References
Advisories
Packages
Other links
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
| Advisory | Severity | Why it's related |
|---|---|---|
| GHSA-8397-8qf6-f2gm vLLM: Phi-4 multimodal audio encoder materializes and retains full attention matrices: a single max-length audio OOM-crashes the vLLM engine | Medium | Also affects vllm |
| GHSA-x567-49g5-p4cf vLLM: Responses API bypasses trust_request_chat_template for chat_template_kwargs | Medium | Also affects vllm |
| GHSA-vvpg-rrww-wpmh vLLM: Permissive default CORS origins allow cross-origin requests to the vLLM HTTP server | Medium | Also affects vllm |
| GHSA-q97c-p368-75c4 vLLM: Out-of-vocabulary prompt token id passes input validation and triggers a CUDA device-side assert (engine crash / DoS) | Medium | Also affects vllm |
| GHSA-h3rc-j82p-2c3h vLLM: Untrusted EPS inputs can trigger denial of service or Ghostscript RCE | High | Also affects vllm |
| GHSA-gq5q-89c7-vhvx vLLM DeepSeek-OCR image processing vulnerable to denial of service | Medium | Also affects vllm |
| GHSA-6757-hggm-hp2w vLLM: Public multimodal SHM handle forgery reaches worker-side `pickle.loads()` with insecure serialization disabled | High | Also affects vllm |
| GHSA-542m-9234-8gvf vLLM: DoS via unbounded chat_template Jinja2 rendering exhausts the renderer thread pool | Medium | Also affects vllm |
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