Описание
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Maliciously constructed statements can lead to hash collisions, resulting in cache reuse, which can interfere with subsequent responses and cause unintended behavior. Prefix caching makes use of Python's built-in hash() function. As of Python 3.12, the behavior of hash(None) has changed to be a predictable constant value. This makes it more feasible that someone could try exploit hash collisions. The impact of a collision would be using cache that was generated using different content. Given knowledge of prompts in use and predictable hashing behavior, someone could intentionally populate the cache using a prompt known to collide with another prompt in use. This issue has been addressed in version 0.7.2 and all users are advised to upgrade. There are no known workarounds for this vulnerability.
A flaw was found in the vllm package. Maliciously constructed statements can lead to hash collisions, resulting in cache reuse, which can interfere with subsequent responses and cause unintended behavior. The impact of a collision would be using a cache that was generated using different content. With the knowledge of the prompts in use and predictable hashing behavior, someone could intentionally populate the cache using a prompt known to collide with another prompt in use.
Меры по смягчению последствий
Using a hashing algorithm that is less prone to collision, such as sha256, for example, would be the best way to avoid the possibility of a collision.
Затронутые пакеты
| Платформа | Пакет | Состояние | Рекомендация | Релиз |
|---|---|---|---|---|
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/bootc-aws-nvidia-rhel9 | Fix deferred | ||
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/bootc-azure-amd-rhel9 | Fix deferred | ||
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/bootc-azure-nvidia-rhel9 | Fix deferred | ||
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/bootc-gcp-nvidia-rhel9 | Fix deferred | ||
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/bootc-ibm-nvidia-rhel9 | Fix deferred | ||
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/bootc-intel-rhel9 | Fix deferred | ||
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/bootc-nvidia-rhel9 | Fix deferred | ||
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/instructlab-amd-rhel9 | Fix deferred | ||
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/instructlab-intel-rhel9 | Fix deferred | ||
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/instructlab-nvidia-rhel9 | Fix deferred |
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Дополнительная информация
Статус:
2.6 Low
CVSS3
Связанные уязвимости
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Maliciously constructed statements can lead to hash collisions, resulting in cache reuse, which can interfere with subsequent responses and cause unintended behavior. Prefix caching makes use of Python's built-in hash() function. As of Python 3.12, the behavior of hash(None) has changed to be a predictable constant value. This makes it more feasible that someone could try exploit hash collisions. The impact of a collision would be using cache that was generated using different content. Given knowledge of prompts in use and predictable hashing behavior, someone could intentionally populate the cache using a prompt known to collide with another prompt in use. This issue has been addressed in version 0.7.2 and all users are advised to upgrade. There are no known workarounds for this vulnerability.
vLLM using built-in hash() from Python 3.12 leads to predictable hash collisions in vLLM prefix cache
vLLM is a high-throughput and memory-efficient inference and serving e ...
vLLM uses Python 3.12 built-in hash() which leads to predictable hash collisions in prefix cache
2.6 Low
CVSS3