CVE-2026-22778 is a critical security vulnerability classified under CWE-532, involving the insertion of sensitive information into log files within the vllm-project's vllm inference engine for large language models. The vulnerability exists in versions 0.8.3 through 0.14.0. When an invalid image is sent to vllm's multimodal endpoint, the Python Imaging Library (PIL) throws an error that is returned to the client, inadvertently leaking a heap memory address. This leak significantly reduces the entropy of Address Space Layout Randomization (ASLR) from approximately 4 billion possible guesses to about 8, making it trivial for an attacker to predict memory locations. This information leak can be chained with a known heap overflow vulnerability in the JPEG2000 decoder used by OpenCV/FFmpeg, enabling an attacker to execute arbitrary code remotely on the affected system. The attack requires no privileges or user interaction, increasing its risk. The vulnerability was publicly disclosed and assigned a CVSS v3.1 score of 9.8 (critical), reflecting its high impact on confidentiality, integrity, and availability. The issue is resolved in vllm version 0.14.1, which no longer leaks heap addresses in error messages and mitigates the attack vector. No known exploits are reported in the wild yet, but the ease of exploitation and critical impact necessitate urgent remediation.

For European organizations, this vulnerability poses a severe risk, especially those leveraging vllm for AI inference and serving large language models with multimodal capabilities. The ability to remotely execute code without authentication or user interaction can lead to full system compromise, data breaches, and disruption of AI services. Confidentiality is at high risk due to heap address leakage, which can be exploited to bypass ASLR and escalate attacks. Integrity and availability are also threatened by potential arbitrary code execution, which could allow attackers to manipulate AI model outputs, corrupt data, or cause denial of service. Organizations in sectors such as finance, healthcare, and critical infrastructure that use AI models for decision-making or automation could face operational and reputational damage. The vulnerability's exploitation could also facilitate lateral movement within networks, increasing the overall attack surface. Given the increasing adoption of AI technologies across Europe, the threat is widespread and urgent.

1. Immediately upgrade all vllm deployments to version 0.14.1 or later, where the vulnerability is fixed. 2. Implement strict input validation and sanitization on all image inputs sent to the multimodal endpoint to prevent malformed or invalid images from triggering errors. 3. Employ network-level protections such as Web Application Firewalls (WAFs) to detect and block suspicious payloads targeting the multimodal endpoint. 4. Monitor logs and network traffic for unusual error messages or patterns indicative of exploitation attempts. 5. Restrict access to the vllm multimodal endpoint to trusted users and networks, using authentication and network segmentation where possible. 6. Regularly update and patch dependencies like OpenCV and FFmpeg to their latest secure versions to mitigate chained vulnerabilities. 7. Conduct security assessments and penetration testing focused on AI inference services to identify and remediate similar issues proactively. 8. Educate development and operations teams about secure error handling practices to avoid leaking sensitive information in logs or responses.