{“lastseen”:””,”description”:”vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, the fix for CVE-2026-22778, which introduced a sanitize_message helper that strips object-repr memory addresses from error messages before they reach the client, is incomplete: several response paths echo str(exc) directly to clients without calling sanitize_message. The unsanitized sites include the Anthropic API router in vllm\/entrypoints\/anthropic\/api_router.py (the POST \/v1\/messages and POST \/v1\/messages\/count_tokens handlers), the Server-Sent Events streaming converter in vllm\/entrypoints\/anthropic\/serving.py, and the realtime speech-to-text WebSocket in vllm\/entrypoints\/speech_to_text\/realtime\/connection.py. These paths catch the exception inside the route coroutine and construct the JSONResponse themselves, bypassing the sanitizing global FastAPI exception handler, and WebSocket frames do not traverse that handler chain at all. Using the same primitive as the parent issue, an unauthenticated attacker can send malformed image bytes through the Anthropic Messages API image content parts so that PIL.Image.open raises an UnidentifiedImageError whose message contains the BytesIO object repr, leaking the heap memory address verbatim in the error.message field of the response body. This vulnerability is fixed in 0.23.1rc0.”,”published”:”2026-06-22T22:09:15.034Z”,”modified”:”2026-06-22T22:09:15.034Z”,”type”:”cve”,”title”:”vLLM: incomplete CVE-2026-22778 fix leaks PIL repr addresses via Anthropic router”,”source”:”GitHub_M”,”references”:”https:\/\/github.com\/vllm-project\/vllm\/security\/advisories\/GHSA-hgg8-fqqc-vfmw\\nhttps:\/\/github.com\/vllm-project\/vllm\/pull\/45119\\nhttps:\/\/github.com\/vllm-project\/vllm\/commit\/94923629729381d7f7c9efde72071a2441f7fd82″,”id”:”CVE-2026-54236″,”bulletinFamily”:””,”cwe”:[“CWE-532″],”cvelist”:null,”sourceData”:”vllm-project vllm \\u003c 0.23.1rc0″,”sourceHref”:””,”cvss”:{“score”:5.3,”severity”:”MEDIUM”,”vector”:”CVSS:3.1\/AV:N\/AC:L\/PR:N\/UI:N\/S:U\/C:L\/I:N\/A:N”,”version”:”3.1″},”cvss2″:{},”cvss3″:{“version”:””,”vectorString”:””,”baseScore”:0,”baseSeverity”:””,”attackVector”:””,”attackComplexity”:””,”privilegesRequired”:””,”userInteraction”:””,”scope”:””,”confidentialityImpact”:””,”integrityImpact”:””,”availabilityImpact”:””,”cvssV3″:{“version”:””,”vectorString”:””,”baseScore”:0,”baseSeverity”:””,”attackVector”:””,”attackComplexity”:””,”privilegesRequired”:””,”userInteraction”:””,”scope”:””,”confidentialityImpact”:””,”integrityImpact”:””,”availabilityImpact”:””}},”href”:””,”category_name”:”CVE”,”post_link”:””,”product”:”vllm”,”version”:”\\u003c 0.23.1rc0″,”vendor”:”vllm-project”,”ai_description”:””,”ai_severity”:””,”ai_vendor”:””,”ai_product”:””,”ai_version”:””,”ai_score”:0}<\/p>\n","protected":false},"excerpt":{"rendered":"
{“lastseen”:””,”description”:”vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, the fix for CVE-2026-22778, which introduced a sanitize_message helper that…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[6,8,22,12,21,13,7,11,5],"class_list":["post-64959","post","type-post","status-publish","format-standard","hentry","category-category_cve","tag-cve","tag-cvss","tag-cvss-53","tag-exploit","tag-medium","tag-news","tag-security","tag-tapic","tag-vulnerability"],"yoast_head":"\n