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    Home»Security»Common Vulnerabilities and Exposures (CVEs)»CVE-2025-46560 – LLaMA LLM Multimodal Tokenizer Resource Exhaustion

    CVE-2025-46560 – LLaMA LLM Multimodal Tokenizer Resource Exhaustion

    April 30, 2025

    CVE ID : CVE-2025-46560

    Published : April 30, 2025, 1:15 a.m. | 1 hour, 52 minutes ago

    Description : vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.8.0 and prior to 0.8.5 are affected by a critical performance vulnerability in the input preprocessing logic of the multimodal tokenizer. The code dynamically replaces placeholder tokens (e.g., , ) with repeated tokens based on precomputed lengths. Due to ​​inefficient list concatenation operations​​, the algorithm exhibits ​​quadratic time complexity (O(n²))​​, allowing malicious actors to trigger resource exhaustion via specially crafted inputs. This issue has been patched in version 0.8.5.

    Severity: 6.5 | MEDIUM

    Visit the link for more details, such as CVSS details, affected products, timeline, and more…

    Source: Read More

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