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• CVE-2026-22777 • published on January 10, 2026 ComfyUI-Manager is an extension designed to enhance the usability of ComfyUI. Prior to versions 3.39.2 and 4.0.5, an attacker can inject special characters into HTTP query parameters to add arbitrary configuration values to the config.ini file. This can lead to security setting tampering or modification of application behavior. This issue has been patched in versions 3.39.2 and 4.0.5.
• CVE-2026-22773 • published on January 10, 2026 vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
• CVE-2026-22773 • published on January 10, 2026 vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
• CVE-2026-22773 • published on January 10, 2026 vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
• CVE-2026-22773 • published on January 10, 2026 vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
• CVE-2026-22773 • published on January 10, 2026 vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
• CVE-2026-22773 • published on January 10, 2026 vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
• CVE-2026-22773 • published on January 10, 2026 vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
• CVE-2026-22773 • published on January 10, 2026 vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
• CVE-2026-22773 • published on January 10, 2026 vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
• CVE-2026-22773 • published on January 10, 2026 vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
• CVE-2026-22773 • published on January 10, 2026 vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
• CVE-2026-22773 • published on January 10, 2026 vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
• CVE-2026-22773 • published on January 10, 2026 vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
• CVE-2026-22773 • published on January 10, 2026 vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
• CVE-2026-22773 • published on January 10, 2026 vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
• CVE-2025-14943 • published on January 10, 2026 The Blog2Social: Social Media Auto Post & Scheduler plugin for WordPress is vulnerable to Sensitive Information Exposure in all versions up to, and including, 8.7.2. This is due to a misconfigured authorization check on the 'getShipItemFullText' function which only verifies that a user has the 'read' capability (Subscriber-level) and a valid nonce, but fails to verify whether the user has permission to access the specific post being requested. This makes it possible for authenticated attackers, with Subscriber-level access and above, to extract data from password-protected, private, or draft posts.
• CVE-2025-14943 • published on January 10, 2026 The Blog2Social: Social Media Auto Post & Scheduler plugin for WordPress is vulnerable to Sensitive Information Exposure in all versions up to, and including, 8.7.2. This is due to a misconfigured authorization check on the 'getShipItemFullText' function which only verifies that a user has the 'read' capability (Subscriber-level) and a valid nonce, but fails to verify whether the user has permission to access the specific post being requested. This makes it possible for authenticated attackers, with Subscriber-level access and above, to extract data from password-protected, private, or draft posts.
• CVE-2025-14943 • published on January 10, 2026 The Blog2Social: Social Media Auto Post & Scheduler plugin for WordPress is vulnerable to Sensitive Information Exposure in all versions up to, and including, 8.7.2. This is due to a misconfigured authorization check on the 'getShipItemFullText' function which only verifies that a user has the 'read' capability (Subscriber-level) and a valid nonce, but fails to verify whether the user has permission to access the specific post being requested. This makes it possible for authenticated attackers, with Subscriber-level access and above, to extract data from password-protected, private, or draft posts.
• CVE-2025-14943 • published on January 10, 2026 The Blog2Social: Social Media Auto Post & Scheduler plugin for WordPress is vulnerable to Sensitive Information Exposure in all versions up to, and including, 8.7.2. This is due to a misconfigured authorization check on the 'getShipItemFullText' function which only verifies that a user has the 'read' capability (Subscriber-level) and a valid nonce, but fails to verify whether the user has permission to access the specific post being requested. This makes it possible for authenticated attackers, with Subscriber-level access and above, to extract data from password-protected, private, or draft posts.