CVE-2026-22778 Scanner

vLLM is a platform used for deploying language models and multimodal processing capabilities. It is utilized in environments where language model handling and AI-based processing are essential. Businesses and research institutions often employ vLLM for efficient model management and multimodal data processing. The platform is beneficial in AI research labs and tech-driven companies focusing on language technologies. Developers and engineers use it for deploying and managing AI models. The platform supports integrations with various AI and machine learning frameworks for enhanced work efficiency.

This Information Disclosure vulnerability in vLLM involves leaking a heap address through error messages when processing invalid images. The vulnerability impacts the platform by reducing Address Space Layout Randomization (ASLR) effectiveness. Attackers can exploit this flaw to access sensitive heap addresses remotely. The disclosure of memory addresses can lead to further exploitation, such as Remote Code Execution. Protecting against such vulnerabilities is critical to maintaining platform security and resilience.

Technical details reveal that the vulnerability resides in how the multimodal endpoint processes invalid images, leaking memory information in the process. Specifically, when invalid image data is submitted, heap address information may be included in the resultant error messages. This is due to inadequate validation checks in handling image input data. The issuing of error messages with detailed memory information facilitates attackers in determining heap locations. These disclosures pose significant security risks if left unmitigated, making patching procedures crucial.

The possible effects of exploiting this vulnerability include significant security bypass capabilities by attackers. An adversary could leverage the disclosed heap addresses to manipulate memory contents more precisely, paving the way for further attacks, such as Remote Code Execution. The reduction of ASLR entropy undermines system protection layers, leaving it vulnerable to calculated exploits. Details revealed through this vulnerability can assist attackers in crafting more sophisticated attack vectors. Consequent breaches could compromise sensitive user data managed by the platform.

REFERENCES

• https://github.com/vllm-project/vllm/security/advisories/GHSA-4r2x-xpjr-7cvv
• https://orca.security/resources/blog/cve-2026-22778-vllm-rce-vulnerability/
• https://nvd.nist.gov/vuln/detail/CVE-2026-22778