Search criteria
45 vulnerabilities found for vllm by vllm-project
CVE-2025-66448 (GCVE-0-2025-66448)
Vulnerability from cvelistv5 – Published: 2025-12-01 22:45 – Updated: 2025-12-02 14:14
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.11.1, vllm has a critical remote code execution vector in a config class named Nemotron_Nano_VL_Config. When vllm loads a model config that contains an auto_map entry, the config class resolves that mapping with get_class_from_dynamic_module(...) and immediately instantiates the returned class. This fetches and executes Python from the remote repository referenced in the auto_map string. Crucially, this happens even when the caller explicitly sets trust_remote_code=False in vllm.transformers_utils.config.get_config. In practice, an attacker can publish a benign-looking frontend repo whose config.json points via auto_map to a separate malicious backend repo; loading the frontend will silently run the backend’s code on the victim host. This vulnerability is fixed in 0.11.1.
Severity ?
7.1 (High)
CWE
- CWE-94 - Improper Control of Generation of Code ('Code Injection')
Assigner
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
< 0.11.1
|
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CVE-2025-62372 (GCVE-0-2025-62372)
Vulnerability from cvelistv5 – Published: 2025-11-21 01:22 – Updated: 2025-11-24 18:11
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before 0.11.1, users can crash the vLLM engine serving multimodal models by passing multimodal embedding inputs with correct ndim but incorrect shape (e.g. hidden dimension is wrong), regardless of whether the model is intended to support such inputs (as defined in the Supported Models page). This issue has been patched in version 0.11.1.
Severity ?
CWE
- CWE-129 - Improper Validation of Array Index
Assigner
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.5.5, < 0.11.1
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CVE-2025-62426 (GCVE-0-2025-62426)
Vulnerability from cvelistv5 – Published: 2025-11-21 01:21 – Updated: 2025-11-24 18:12
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before 0.11.1, the /v1/chat/completions and /tokenize endpoints allow a chat_template_kwargs request parameter that is used in the code before it is properly validated against the chat template. With the right chat_template_kwargs parameters, it is possible to block processing of the API server for long periods of time, delaying all other requests. This issue has been patched in version 0.11.1.
Severity ?
6.5 (Medium)
CWE
- CWE-770 - Allocation of Resources Without Limits or Throttling
Assigner
References
| URL | Tags | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.5.5, < 0.11.1
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CVE-2025-62164 (GCVE-0-2025-62164)
Vulnerability from cvelistv5 – Published: 2025-11-21 01:18 – Updated: 2025-11-24 18:12
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). From versions 0.10.2 to before 0.11.1, a memory corruption vulnerability could lead to a crash (denial-of-service) and potentially remote code execution (RCE), exists in the Completions API endpoint. When processing user-supplied prompt embeddings, the endpoint loads serialized tensors using torch.load() without sufficient validation. Due to a change introduced in PyTorch 2.8.0, sparse tensor integrity checks are disabled by default. As a result, maliciously crafted tensors can bypass internal bounds checks and trigger an out-of-bounds memory write during the call to to_dense(). This memory corruption can crash vLLM and potentially lead to code execution on the server hosting vLLM. This issue has been patched in version 0.11.1.
Severity ?
8.8 (High)
CWE
Assigner
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.10.2, < 0.11.1
|
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CVE-2025-59425 (GCVE-0-2025-59425)
Vulnerability from cvelistv5 – Published: 2025-10-07 14:06 – Updated: 2025-10-07 15:28
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). Before version 0.11.0rc2, the API key support in vLLM performs validation using a method that was vulnerable to a timing attack. API key validation uses a string comparison that takes longer the more characters the provided API key gets correct. Data analysis across many attempts could allow an attacker to determine when it finds the next correct character in the key sequence. Deployments relying on vLLM's built-in API key validation are vulnerable to authentication bypass using this technique. Version 0.11.0rc2 fixes the issue.
Severity ?
7.5 (High)
CWE
- CWE-385 - Covert Timing Channel
Assigner
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
< 0.11.0rc2
|
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CVE-2025-48956 (GCVE-0-2025-48956)
Vulnerability from cvelistv5 – Published: 2025-08-21 14:41 – Updated: 2025-08-21 15:02
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.10.1.1, a Denial of Service (DoS) vulnerability can be triggered by sending a single HTTP GET request with an extremely large header to an HTTP endpoint. This results in server memory exhaustion, potentially leading to a crash or unresponsiveness. The attack does not require authentication, making it exploitable by any remote user. This vulnerability is fixed in 0.10.1.1.
Severity ?
7.5 (High)
CWE
- CWE-400 - Uncontrolled Resource Consumption
Assigner
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.1.0, < 0.10.1.1
|
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CVE-2025-48944 (GCVE-0-2025-48944)
Vulnerability from cvelistv5 – Published: 2025-05-30 18:38 – Updated: 2025-05-30 18:56
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). In version 0.8.0 up to but excluding 0.9.0, the vLLM backend used with the /v1/chat/completions OpenAPI endpoint fails to validate unexpected or malformed input in the "pattern" and "type" fields when the tools functionality is invoked. These inputs are not validated before being compiled or parsed, causing a crash of the inference worker with a single request. The worker will remain down until it is restarted. Version 0.9.0 fixes the issue.
Severity ?
6.5 (Medium)
CWE
- CWE-20 - Improper Input Validation
Assigner
References
| URL | Tags | |||||||
|---|---|---|---|---|---|---|---|---|
|
||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.8.0, < 0.9.0
|
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CVE-2025-48943 (GCVE-0-2025-48943)
Vulnerability from cvelistv5 – Published: 2025-05-30 18:36 – Updated: 2025-05-30 18:56
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). Version 0.8.0 up to but excluding 0.9.0 have a Denial of Service (ReDoS) that causes the vLLM server to crash if an invalid regex was provided while using structured output. This vulnerability is similar to GHSA-6qc9-v4r8-22xg/CVE-2025-48942, but for regex instead of a JSON schema. Version 0.9.0 fixes the issue.
Severity ?
6.5 (Medium)
CWE
- CWE-248 - Uncaught Exception
Assigner
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.8.0, < 0.9.0
|
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CVE-2025-48942 (GCVE-0-2025-48942)
Vulnerability from cvelistv5 – Published: 2025-05-30 18:33 – Updated: 2025-05-30 20:37
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). In versions 0.8.0 up to but excluding 0.9.0, hitting the /v1/completions API with a invalid json_schema as a Guided Param kills the vllm server. This vulnerability is similar GHSA-9hcf-v7m4-6m2j/CVE-2025-48943, but for regex instead of a JSON schema. Version 0.9.0 fixes the issue.
Severity ?
6.5 (Medium)
CWE
- CWE-248 - Uncaught Exception
Assigner
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.8.0, < 0.9.0
|
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CVE-2025-48887 (GCVE-0-2025-48887)
Vulnerability from cvelistv5 – Published: 2025-05-30 17:36 – Updated: 2025-05-30 17:58
VLAI?
Summary
vLLM, an inference and serving engine for large language models (LLMs), has a Regular Expression Denial of Service (ReDoS) vulnerability in the file `vllm/entrypoints/openai/tool_parsers/pythonic_tool_parser.py` of versions 0.6.4 up to but excluding 0.9.0. The root cause is the use of a highly complex and nested regular expression for tool call detection, which can be exploited by an attacker to cause severe performance degradation or make the service unavailable. The pattern contains multiple nested quantifiers, optional groups, and inner repetitions which make it vulnerable to catastrophic backtracking. Version 0.9.0 contains a patch for the issue.
Severity ?
6.5 (Medium)
CWE
- CWE-1333 - Inefficient Regular Expression Complexity
Assigner
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.6.4, < 0.9.0
|
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CVE-2025-46722 (GCVE-0-2025-46722)
Vulnerability from cvelistv5 – Published: 2025-05-29 16:36 – Updated: 2025-05-29 18:13
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). In versions starting from 0.7.0 to before 0.9.0, in the file vllm/multimodal/hasher.py, the MultiModalHasher class has a security and data integrity issue in its image hashing method. Currently, it serializes PIL.Image.Image objects using only obj.tobytes(), which returns only the raw pixel data, without including metadata such as the image’s shape (width, height, mode). As a result, two images of different sizes (e.g., 30x100 and 100x30) with the same pixel byte sequence could generate the same hash value. This may lead to hash collisions, incorrect cache hits, and even data leakage or security risks. This issue has been patched in version 0.9.0.
Severity ?
4.2 (Medium)
CWE
Assigner
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.7.0, < 0.9.0
|
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CVE-2025-46570 (GCVE-0-2025-46570)
Vulnerability from cvelistv5 – Published: 2025-05-29 16:32 – Updated: 2025-05-29 18:05
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). Prior to version 0.9.0, when a new prompt is processed, if the PageAttention mechanism finds a matching prefix chunk, the prefill process speeds up, which is reflected in the TTFT (Time to First Token). These timing differences caused by matching chunks are significant enough to be recognized and exploited. This issue has been patched in version 0.9.0.
Severity ?
CWE
- CWE-208 - Observable Timing Discrepancy
Assigner
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
< 0.9.0
|
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CVE-2025-47277 (GCVE-0-2025-47277)
Vulnerability from cvelistv5 – Published: 2025-05-20 17:32 – Updated: 2025-05-20 17:52
VLAI?
Summary
vLLM, an inference and serving engine for large language models (LLMs), has an issue in versions 0.6.5 through 0.8.4 that ONLY impacts environments using the `PyNcclPipe` KV cache transfer integration with the V0 engine. No other configurations are affected. vLLM supports the use of the `PyNcclPipe` class to establish a peer-to-peer communication domain for data transmission between distributed nodes. The GPU-side KV-Cache transmission is implemented through the `PyNcclCommunicator` class, while CPU-side control message passing is handled via the `send_obj` and `recv_obj` methods on the CPU side. The intention was that this interface should only be exposed to a private network using the IP address specified by the `--kv-ip` CLI parameter. The vLLM documentation covers how this must be limited to a secured network. The default and intentional behavior from PyTorch is that the `TCPStore` interface listens on ALL interfaces, regardless of what IP address is provided. The IP address given was only used as a client-side address to use. vLLM was fixed to use a workaround to force the `TCPStore` instance to bind its socket to a specified private interface. As of version 0.8.5, vLLM limits the `TCPStore` socket to the private interface as configured.
Severity ?
9.8 (Critical)
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.6.5, < 0.8.5
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CVE-2025-30165 (GCVE-0-2025-30165)
Vulnerability from cvelistv5 – Published: 2025-05-06 16:53 – Updated: 2025-05-06 17:26
VLAI?
Summary
vLLM is an inference and serving engine for large language models. In a multi-node vLLM deployment using the V0 engine, vLLM uses ZeroMQ for some multi-node communication purposes. The secondary vLLM hosts open a `SUB` ZeroMQ socket and connect to an `XPUB` socket on the primary vLLM host. When data is received on this `SUB` socket, it is deserialized with `pickle`. This is unsafe, as it can be abused to execute code on a remote machine. Since the vulnerability exists in a client that connects to the primary vLLM host, this vulnerability serves as an escalation point. If the primary vLLM host is compromised, this vulnerability could be used to compromise the rest of the hosts in the vLLM deployment. Attackers could also use other means to exploit the vulnerability without requiring access to the primary vLLM host. One example would be the use of ARP cache poisoning to redirect traffic to a malicious endpoint used to deliver a payload with arbitrary code to execute on the target machine. Note that this issue only affects the V0 engine, which has been off by default since v0.8.0. Further, the issue only applies to a deployment using tensor parallelism across multiple hosts, which we do not expect to be a common deployment pattern. Since V0 is has been off by default since v0.8.0 and the fix is fairly invasive, the maintainers of vLLM have decided not to fix this issue. Instead, the maintainers recommend that users ensure their environment is on a secure network in case this pattern is in use. The V1 engine is not affected by this issue.
Severity ?
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.5.2, <= 0.8.5.post1
|
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CVE-2025-32444 (GCVE-0-2025-32444)
Vulnerability from cvelistv5 – Published: 2025-04-30 00:25 – Updated: 2025-04-30 13:08
VLAI?
Summary
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.6.5 and prior to 0.8.5, having vLLM integration with mooncake, are vulnerable to remote code execution due to using pickle based serialization over unsecured ZeroMQ sockets. The vulnerable sockets were set to listen on all network interfaces, increasing the likelihood that an attacker is able to reach the vulnerable ZeroMQ sockets to carry out an attack. vLLM instances that do not make use of the mooncake integration are not vulnerable. This issue has been patched in version 0.8.5.
Severity ?
10 (Critical)
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.6.5, < 0.8.5
|
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CVE-2025-66448 (GCVE-0-2025-66448)
Vulnerability from nvd – Published: 2025-12-01 22:45 – Updated: 2025-12-02 14:14
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.11.1, vllm has a critical remote code execution vector in a config class named Nemotron_Nano_VL_Config. When vllm loads a model config that contains an auto_map entry, the config class resolves that mapping with get_class_from_dynamic_module(...) and immediately instantiates the returned class. This fetches and executes Python from the remote repository referenced in the auto_map string. Crucially, this happens even when the caller explicitly sets trust_remote_code=False in vllm.transformers_utils.config.get_config. In practice, an attacker can publish a benign-looking frontend repo whose config.json points via auto_map to a separate malicious backend repo; loading the frontend will silently run the backend’s code on the victim host. This vulnerability is fixed in 0.11.1.
Severity ?
7.1 (High)
CWE
- CWE-94 - Improper Control of Generation of Code ('Code Injection')
Assigner
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
< 0.11.1
|
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CVE-2025-62372 (GCVE-0-2025-62372)
Vulnerability from nvd – Published: 2025-11-21 01:22 – Updated: 2025-11-24 18:11
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before 0.11.1, users can crash the vLLM engine serving multimodal models by passing multimodal embedding inputs with correct ndim but incorrect shape (e.g. hidden dimension is wrong), regardless of whether the model is intended to support such inputs (as defined in the Supported Models page). This issue has been patched in version 0.11.1.
Severity ?
CWE
- CWE-129 - Improper Validation of Array Index
Assigner
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.5.5, < 0.11.1
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CVE-2025-62426 (GCVE-0-2025-62426)
Vulnerability from nvd – Published: 2025-11-21 01:21 – Updated: 2025-11-24 18:12
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before 0.11.1, the /v1/chat/completions and /tokenize endpoints allow a chat_template_kwargs request parameter that is used in the code before it is properly validated against the chat template. With the right chat_template_kwargs parameters, it is possible to block processing of the API server for long periods of time, delaying all other requests. This issue has been patched in version 0.11.1.
Severity ?
6.5 (Medium)
CWE
- CWE-770 - Allocation of Resources Without Limits or Throttling
Assigner
References
| URL | Tags | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.5.5, < 0.11.1
|
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CVE-2025-62164 (GCVE-0-2025-62164)
Vulnerability from nvd – Published: 2025-11-21 01:18 – Updated: 2025-11-24 18:12
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). From versions 0.10.2 to before 0.11.1, a memory corruption vulnerability could lead to a crash (denial-of-service) and potentially remote code execution (RCE), exists in the Completions API endpoint. When processing user-supplied prompt embeddings, the endpoint loads serialized tensors using torch.load() without sufficient validation. Due to a change introduced in PyTorch 2.8.0, sparse tensor integrity checks are disabled by default. As a result, maliciously crafted tensors can bypass internal bounds checks and trigger an out-of-bounds memory write during the call to to_dense(). This memory corruption can crash vLLM and potentially lead to code execution on the server hosting vLLM. This issue has been patched in version 0.11.1.
Severity ?
8.8 (High)
CWE
Assigner
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.10.2, < 0.11.1
|
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CVE-2025-59425 (GCVE-0-2025-59425)
Vulnerability from nvd – Published: 2025-10-07 14:06 – Updated: 2025-10-07 15:28
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). Before version 0.11.0rc2, the API key support in vLLM performs validation using a method that was vulnerable to a timing attack. API key validation uses a string comparison that takes longer the more characters the provided API key gets correct. Data analysis across many attempts could allow an attacker to determine when it finds the next correct character in the key sequence. Deployments relying on vLLM's built-in API key validation are vulnerable to authentication bypass using this technique. Version 0.11.0rc2 fixes the issue.
Severity ?
7.5 (High)
CWE
- CWE-385 - Covert Timing Channel
Assigner
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
< 0.11.0rc2
|
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CVE-2025-48956 (GCVE-0-2025-48956)
Vulnerability from nvd – Published: 2025-08-21 14:41 – Updated: 2025-08-21 15:02
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.10.1.1, a Denial of Service (DoS) vulnerability can be triggered by sending a single HTTP GET request with an extremely large header to an HTTP endpoint. This results in server memory exhaustion, potentially leading to a crash or unresponsiveness. The attack does not require authentication, making it exploitable by any remote user. This vulnerability is fixed in 0.10.1.1.
Severity ?
7.5 (High)
CWE
- CWE-400 - Uncontrolled Resource Consumption
Assigner
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.1.0, < 0.10.1.1
|
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CVE-2025-48944 (GCVE-0-2025-48944)
Vulnerability from nvd – Published: 2025-05-30 18:38 – Updated: 2025-05-30 18:56
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). In version 0.8.0 up to but excluding 0.9.0, the vLLM backend used with the /v1/chat/completions OpenAPI endpoint fails to validate unexpected or malformed input in the "pattern" and "type" fields when the tools functionality is invoked. These inputs are not validated before being compiled or parsed, causing a crash of the inference worker with a single request. The worker will remain down until it is restarted. Version 0.9.0 fixes the issue.
Severity ?
6.5 (Medium)
CWE
- CWE-20 - Improper Input Validation
Assigner
References
| URL | Tags | |||||||
|---|---|---|---|---|---|---|---|---|
|
||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.8.0, < 0.9.0
|
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CVE-2025-48943 (GCVE-0-2025-48943)
Vulnerability from nvd – Published: 2025-05-30 18:36 – Updated: 2025-05-30 18:56
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). Version 0.8.0 up to but excluding 0.9.0 have a Denial of Service (ReDoS) that causes the vLLM server to crash if an invalid regex was provided while using structured output. This vulnerability is similar to GHSA-6qc9-v4r8-22xg/CVE-2025-48942, but for regex instead of a JSON schema. Version 0.9.0 fixes the issue.
Severity ?
6.5 (Medium)
CWE
- CWE-248 - Uncaught Exception
Assigner
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.8.0, < 0.9.0
|
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CVE-2025-48942 (GCVE-0-2025-48942)
Vulnerability from nvd – Published: 2025-05-30 18:33 – Updated: 2025-05-30 20:37
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). In versions 0.8.0 up to but excluding 0.9.0, hitting the /v1/completions API with a invalid json_schema as a Guided Param kills the vllm server. This vulnerability is similar GHSA-9hcf-v7m4-6m2j/CVE-2025-48943, but for regex instead of a JSON schema. Version 0.9.0 fixes the issue.
Severity ?
6.5 (Medium)
CWE
- CWE-248 - Uncaught Exception
Assigner
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.8.0, < 0.9.0
|
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CVE-2025-48887 (GCVE-0-2025-48887)
Vulnerability from nvd – Published: 2025-05-30 17:36 – Updated: 2025-05-30 17:58
VLAI?
Summary
vLLM, an inference and serving engine for large language models (LLMs), has a Regular Expression Denial of Service (ReDoS) vulnerability in the file `vllm/entrypoints/openai/tool_parsers/pythonic_tool_parser.py` of versions 0.6.4 up to but excluding 0.9.0. The root cause is the use of a highly complex and nested regular expression for tool call detection, which can be exploited by an attacker to cause severe performance degradation or make the service unavailable. The pattern contains multiple nested quantifiers, optional groups, and inner repetitions which make it vulnerable to catastrophic backtracking. Version 0.9.0 contains a patch for the issue.
Severity ?
6.5 (Medium)
CWE
- CWE-1333 - Inefficient Regular Expression Complexity
Assigner
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.6.4, < 0.9.0
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CVE-2025-46722 (GCVE-0-2025-46722)
Vulnerability from nvd – Published: 2025-05-29 16:36 – Updated: 2025-05-29 18:13
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). In versions starting from 0.7.0 to before 0.9.0, in the file vllm/multimodal/hasher.py, the MultiModalHasher class has a security and data integrity issue in its image hashing method. Currently, it serializes PIL.Image.Image objects using only obj.tobytes(), which returns only the raw pixel data, without including metadata such as the image’s shape (width, height, mode). As a result, two images of different sizes (e.g., 30x100 and 100x30) with the same pixel byte sequence could generate the same hash value. This may lead to hash collisions, incorrect cache hits, and even data leakage or security risks. This issue has been patched in version 0.9.0.
Severity ?
4.2 (Medium)
CWE
Assigner
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.7.0, < 0.9.0
|
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CVE-2025-46570 (GCVE-0-2025-46570)
Vulnerability from nvd – Published: 2025-05-29 16:32 – Updated: 2025-05-29 18:05
VLAI?
Summary
vLLM is an inference and serving engine for large language models (LLMs). Prior to version 0.9.0, when a new prompt is processed, if the PageAttention mechanism finds a matching prefix chunk, the prefill process speeds up, which is reflected in the TTFT (Time to First Token). These timing differences caused by matching chunks are significant enough to be recognized and exploited. This issue has been patched in version 0.9.0.
Severity ?
CWE
- CWE-208 - Observable Timing Discrepancy
Assigner
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
< 0.9.0
|
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CVE-2025-47277 (GCVE-0-2025-47277)
Vulnerability from nvd – Published: 2025-05-20 17:32 – Updated: 2025-05-20 17:52
VLAI?
Summary
vLLM, an inference and serving engine for large language models (LLMs), has an issue in versions 0.6.5 through 0.8.4 that ONLY impacts environments using the `PyNcclPipe` KV cache transfer integration with the V0 engine. No other configurations are affected. vLLM supports the use of the `PyNcclPipe` class to establish a peer-to-peer communication domain for data transmission between distributed nodes. The GPU-side KV-Cache transmission is implemented through the `PyNcclCommunicator` class, while CPU-side control message passing is handled via the `send_obj` and `recv_obj` methods on the CPU side. The intention was that this interface should only be exposed to a private network using the IP address specified by the `--kv-ip` CLI parameter. The vLLM documentation covers how this must be limited to a secured network. The default and intentional behavior from PyTorch is that the `TCPStore` interface listens on ALL interfaces, regardless of what IP address is provided. The IP address given was only used as a client-side address to use. vLLM was fixed to use a workaround to force the `TCPStore` instance to bind its socket to a specified private interface. As of version 0.8.5, vLLM limits the `TCPStore` socket to the private interface as configured.
Severity ?
9.8 (Critical)
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.6.5, < 0.8.5
|
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CVE-2025-30165 (GCVE-0-2025-30165)
Vulnerability from nvd – Published: 2025-05-06 16:53 – Updated: 2025-05-06 17:26
VLAI?
Summary
vLLM is an inference and serving engine for large language models. In a multi-node vLLM deployment using the V0 engine, vLLM uses ZeroMQ for some multi-node communication purposes. The secondary vLLM hosts open a `SUB` ZeroMQ socket and connect to an `XPUB` socket on the primary vLLM host. When data is received on this `SUB` socket, it is deserialized with `pickle`. This is unsafe, as it can be abused to execute code on a remote machine. Since the vulnerability exists in a client that connects to the primary vLLM host, this vulnerability serves as an escalation point. If the primary vLLM host is compromised, this vulnerability could be used to compromise the rest of the hosts in the vLLM deployment. Attackers could also use other means to exploit the vulnerability without requiring access to the primary vLLM host. One example would be the use of ARP cache poisoning to redirect traffic to a malicious endpoint used to deliver a payload with arbitrary code to execute on the target machine. Note that this issue only affects the V0 engine, which has been off by default since v0.8.0. Further, the issue only applies to a deployment using tensor parallelism across multiple hosts, which we do not expect to be a common deployment pattern. Since V0 is has been off by default since v0.8.0 and the fix is fairly invasive, the maintainers of vLLM have decided not to fix this issue. Instead, the maintainers recommend that users ensure their environment is on a secure network in case this pattern is in use. The V1 engine is not affected by this issue.
Severity ?
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
| URL | Tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.5.2, <= 0.8.5.post1
|
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CVE-2025-32444 (GCVE-0-2025-32444)
Vulnerability from nvd – Published: 2025-04-30 00:25 – Updated: 2025-04-30 13:08
VLAI?
Summary
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.6.5 and prior to 0.8.5, having vLLM integration with mooncake, are vulnerable to remote code execution due to using pickle based serialization over unsecured ZeroMQ sockets. The vulnerable sockets were set to listen on all network interfaces, increasing the likelihood that an attacker is able to reach the vulnerable ZeroMQ sockets to carry out an attack. vLLM instances that do not make use of the mooncake integration are not vulnerable. This issue has been patched in version 0.8.5.
Severity ?
10 (Critical)
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.6.5, < 0.8.5
|
{
"containers": {
"adp": [
{
"metrics": [
{
"other": {
"content": {
"id": "CVE-2025-32444",
"options": [
{
"Exploitation": "none"
},
{
"Automatable": "yes"
},
{
"Technical Impact": "total"
}
],
"role": "CISA Coordinator",
"timestamp": "2025-04-30T13:08:21.425422Z",
"version": "2.0.3"
},
"type": "ssvc"
}
}
],
"providerMetadata": {
"dateUpdated": "2025-04-30T13:08:35.928Z",
"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"shortName": "CISA-ADP"
},
"title": "CISA ADP Vulnrichment"
}
],
"cna": {
"affected": [
{
"product": "vllm",
"vendor": "vllm-project",
"versions": [
{
"status": "affected",
"version": "\u003e= 0.6.5, \u003c 0.8.5"
}
]
}
],
"descriptions": [
{
"lang": "en",
"value": "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.6.5 and prior to 0.8.5, having vLLM integration with mooncake, are vulnerable to remote code execution due to using pickle based serialization over unsecured ZeroMQ sockets. The vulnerable sockets were set to listen on all network interfaces, increasing the likelihood that an attacker is able to reach the vulnerable ZeroMQ sockets to carry out an attack. vLLM instances that do not make use of the mooncake integration are not vulnerable. This issue has been patched in version 0.8.5."
}
],
"metrics": [
{
"cvssV3_1": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 10,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "CHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H",
"version": "3.1"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-502",
"description": "CWE-502: Deserialization of Untrusted Data",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2025-04-30T00:25:00.655Z",
"orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
"shortName": "GitHub_M"
},
"references": [
{
"name": "https://github.com/vllm-project/vllm/security/advisories/GHSA-hj4w-hm2g-p6w5",
"tags": [
"x_refsource_CONFIRM"
],
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-hj4w-hm2g-p6w5"
},
{
"name": "https://github.com/vllm-project/vllm/security/advisories/GHSA-x3m8-f7g5-qhm7",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-x3m8-f7g5-qhm7"
},
{
"name": "https://github.com/vllm-project/vllm/commit/a5450f11c95847cf51a17207af9a3ca5ab569b2c",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/vllm-project/vllm/commit/a5450f11c95847cf51a17207af9a3ca5ab569b2c"
},
{
"name": "https://github.com/vllm-project/vllm/blob/32b14baf8a1f7195ca09484de3008063569b43c5/vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py#L179",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/vllm-project/vllm/blob/32b14baf8a1f7195ca09484de3008063569b43c5/vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py#L179"
}
],
"source": {
"advisory": "GHSA-hj4w-hm2g-p6w5",
"discovery": "UNKNOWN"
},
"title": "vLLM Vulnerable to Remote Code Execution via Mooncake Integration"
}
},
"cveMetadata": {
"assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
"assignerShortName": "GitHub_M",
"cveId": "CVE-2025-32444",
"datePublished": "2025-04-30T00:25:00.655Z",
"dateReserved": "2025-04-08T10:54:58.369Z",
"dateUpdated": "2025-04-30T13:08:35.928Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.1"
}