CVE-2026-111392 in SGLang
Summary
by MITRE • 10/16/2026
SGLang through 0.5.21 trusts caller-supplied mm_hashes values as the multimodal prefix-cache key, allowing remote attackers to poison cached KV entries across requests. Attackers can send a different image under a known or predicted hash via /generate to disclose another request's image content or corrupt victims' responses.
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Analysis
by VulDB Data Team • 10/16/2026
The vulnerability in SGLang versions through 0.5.21 stems from an insecure trust relationship regarding multimodal prefix cache keys, specifically the mm_hashes parameter. In this architecture, the system relies on these hashes to identify and retrieve cached key-value (KV) entries for efficient processing of subsequent requests with similar inputs. The critical flaw lies in the fact that SGLang accepts caller-supplied mm_hashes values directly as identifiers without sufficient validation or integrity checks against the actual content being processed. This design decision creates a scenario where an attacker can manipulate the cache lookup mechanism by providing arbitrary hash values, effectively bypassing the intended isolation between different user requests and multimodal inputs.
From a technical perspective, this flaw constitutes a form of cache poisoning that exploits the deterministic nature of hashing algorithms used for prefix caching. When a request is made to the /generate endpoint with a specific mm_hash, the system uses this value to locate existing cached KV entries associated with previous images or prompts sharing that hash. Because the attacker controls the input hash, they can craft requests using hashes known to be associated with other users' data. By sending an image under a predicted or previously observed hash, the attacker forces the model to retrieve and utilize cache entries belonging to another user's session. This breaks the fundamental security assumption of multi-tenancy within the inference engine, allowing for cross-request data leakage and state corruption.
The operational impact of this vulnerability is severe, primarily affecting confidentiality and integrity. An adversary can exploit this mechanism to disclose sensitive image content from other requests by forcing the model to process cached visual features associated with a victim's previous inputs. This could lead to the exposure of proprietary images, personal identifiable information contained within visuals, or confidential business graphics that were previously processed under different sessions. Furthermore, the ability to corrupt victims' responses introduces an integrity risk where attackers can inject misleading context into ongoing conversations by manipulating which cached tokens are retrieved during generation. This not only degrades service quality but can also be leveraged for more sophisticated attacks such as prompt injection or model manipulation if the poisoned cache influences downstream decision-making processes.
This vulnerability aligns with CWE-20 Improper Input Validation, specifically regarding the failure to verify that supplied identifiers correspond to their intended resources before use in critical system functions like caching. It also maps to MITRE ATT&CK techniques related to Cache Poisoning and potentially Data Injection if used to alter model behavior. The lack of cryptographic binding between the actual image content and its cache key allows for this substitution attack, highlighting a common pitfall in systems that prioritize performance optimization over strict input sanitization in caching layers.
Mitigation strategies should focus on implementing robust validation mechanisms for all inputs affecting system state or resource allocation. For SGLang users, upgrading to version 0.5.22 or later is the primary remediation step as it addresses this trust boundary issue by ensuring that cache keys are derived securely from actual content rather than relying solely on caller-supplied values. In environments where immediate patching is not feasible, network-level controls such as rate limiting and strict input size restrictions can help mitigate the frequency of exploitation attempts. Additionally, implementing cryptographic verification mechanisms to bind image data directly to its corresponding hash before cache lookup would prevent attackers from reusing hashes associated with different content. Organizations should also audit their multimodal processing pipelines for similar patterns where external inputs influence internal state management without adequate integrity checks.