GHSA-7m6h-x95x-82q5 CVE-2026-73558 PYSEC-2026-3935
vLLM: Cross-User Data Leak Vulnerability
- Severity
- 5.3 / 10
- Medium · CVSS 3.1
- Exploit chance (EPSS)
- 0.41%
- Chance of exploitation in the next 30 days. Higher than 35.70% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-190 Integer Overflow or Wraparound
- Fix
0.27.0- Upgrade vllm to this version or later
Description
Summary
An integer overflow in the act_and_mul_kernel kernel can cause the output of one user request to be incorporated into the response of another request within the same inference batch. Under certain conditions, the last request in a batch can receive a partial or complete copy of the first user's inference result, resulting in cross-user data leakage.
Details
The root cause is an integer overflow in the expression blockIdx.x * 2 * d at https://github.com/vllm-project/vllm/blob/ff712f6447093d07747c88680b9d006b119f5890/csrc/activation_kernels.cu#L82.
As a result, the computation for one user (User A) can incorrectly consume input data from another user (User B). In particular, when 2^32 is divisible by d, the overflow can cause User A's output to contain portions of User B's inference result. In some cases, User B's response may be copied entirely into User A's response.
This constitutes a severe cross-user information disclosure vulnerability and is straightforward to trigger.
PoC
We reproduced the issue using meta-llama/Llama-3.2-1B-Instruct, for which d = 8192.
Using the following configuration:
Batch size: 17 Sequence length: 16384
The final response in the batch becomes an exact copy of the first response in the batch, demonstrating complete cross-user data leakage.
Impact
This vulnerability enables cross-user information disclosure. An attacker can intentionally craft requests that are processed within the same inference batch as a victim's request and cause the victim's inference output to be copied into the attacker's response.
As a result, sensitive information contained in another user's model response may be exposed to an unauthorized party.
Versions
For versions prior and equal to 0.21.0, the bug is in csrc/activation_kernels.cu, and for versions later than 0.21.0, the bug is in csrc/libtorch_stable/activation_kernels.cu.
Affected versions
PyPI
| Package | Vulnerable | Fixed in |
|---|---|---|
| vllm | < 0.27.0 |
0.27.0 |
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 → 0.27.0
- pip
pip install --upgrade "vllm>=0.27.0"- poetry
poetry add "vllm>=0.27.0"- uv
uv add "vllm>=0.27.0"
Severity breakdown
CVSS 3.1 5.3
CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:N/A:N
- Attack vector
- Network
- Attack complexity
- High
- Privileges required
- None
- User interaction
- Required
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- None
- Availability
- None
Weakness
CWE-190: Integer Overflow or Wraparound
The product performs a calculation that can produce an integer overflow or wraparound when the logic assumes that the resulting value will always be larger than the original value. This occurs when an integer value is incremented to a value that is too large to store in the associated representation. When this occurs, the value may become a very small or negative number.
How to prevent it
- Requirements: Ensure that all protocols are strictly defined, such that all out-of-bounds behavior can be identified simply, and require strict conformance to the protocol.
- Requirements: Use a language that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid. If possible, choose a language or compiler that performs automatic bounds checking.
- Architecture and Design: Use a vetted library or framework that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid [REF-1482]. Use libraries or frameworks that make it easier to handle numbers without unexpected consequences. Examples include safe integer handling packages such as SafeInt (C++) or IntegerLib (C or C++). [REF-106]
- Implementation: Perform input validation on any numeric input by ensuring that it is within the expected range. Enforce that the input meets both the minimum and maximum requirements for the expected range. Use unsigned integers where possible. This makes it easier to perform validation for integer overflows. When signed integers are required, ensure that the range check includes minimum values as well as maximum values.
- Implementation: Understand the programming language's underlying representation and how it interacts with numeric calculation (CWE-681). Pay close attention to byte size discrepancies, precision, signed/unsigned distinctions, truncation, conversion and casting between types, "not-a-number" calculations, and how the language handles numbers that are too large or too small for its underlying representation. [REF-7] Also be careful to account for 32-bit, 64-bit, and other potential differences that may affect the numeric representation.
- 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.
- Implementation: Examine compiler warnings closely and eliminate problems with potential security implications, such as signed / unsigned mismatch in memory operations, or use of uninitialized variables. Even if the weakness is rarely exploitable, a single failure may lead to the compromise of the entire system.
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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