CVE-2026-93034

SGLang through 0.5.22 with the Mooncake disaggregation backend contains an uncontrolled memory allocation vulnerability in handle_staging_rsp(). Unauthenticated network-adjacent attackers can exhaust Prefill memory by sending STAGING_RSP messages with huge chunk_idx values.

SGLang through 0.5.22 contains a denial of service vulnerability that allows API clients to abort other users' in-flight generation requests by supplying a short client-controlled rid. Attackers can submit streaming /generate requests with single hex character rids and disconnect, triggering prefix-matching abort logic that terminates all other clients' requests.

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.

SGLang 0.5.10 through 0.5.22 contains a code injection vulnerability in _evaluate_filter() that allows remote attackers to execute Python code by escaping the eval() sandbox through re.search __globals__. Attackers can send a crafted filter expression to the unauthenticated /dumper/configure endpoint when DUMPER_SERVER_PORT is set, running code in the scheduler process on the next forward pass.

SGLang through 0.5.20 in prefill/decode disaggregation mode fails to validate duplicate bootstrap_room fields in /generate requests with Mooncake KV transfer backend. Unauthenticated attackers can send concurrent requests with identical bootstrap_room values to crash scheduler processes or hang other users' requests until transfer timeout.

SGLang's multimodal generation runtime is vulnerable to unauthenticated arbitrary code execution because the disaggregated-diffusion orchestrator's DiffusionServer binds an unauthenticated ZeroMQ ROUTER socket to a network interface and passes the final frame of received multipart messages directly to pickle.loads() before any validation occurs.

SGLang allows unauthenticated pickle deserialization through /update_weights_from_tensor when no auth keys are configured, and the SafeUnpickler policy can be bypassed because builtins.import and builtins.getattr are resolvable, enabling code execution via pickle REDUCE.

SGLang contains a remote code execution (RCE) vulnerability when loading model weights from a HuggingFace repository. In the /update_weights_from_disk endpoint, torch.load with weights_only=False allows pickle deserialization of .bin files.

SGLang contains a remote code execution (RCE) vulnerability in the optional dumper subsystem. When DUMPER_SERVER_PORT is set, a sandbox escape is possible, enabling code execution on inference requests.

SGLang contains an unauthenticated remote code execution (RCE) vulnerability in the /load_lora_adapter_from_tensors endpoint. It bypasses SafeUnpickler's incomplete denylist, allowing arbitrary command execution through crafted base64-encoded pickle payloads.