CVE-2026-94570

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.

The vulnerability in SGLang involves an expert-parallel backup subsystem that exposes a ZeroMQ PULL socket on a routable network interface without authentication or deserialization safeguards. This allows an attacker to provide a malicious pickle file, resulting in unauthenticated remote code execution when the feature is enabled and the service is reachable over the network.

SGLang versions through 0.5.20 contain an unbounded memory allocation vulnerability in handle_staging_req() that fails to validate chunk_idx from ZMQ STAGING_REQ frames in prefill/decode disaggregation deployments. Attackers with access to the decode engine's internal ZMQ rank port can send a frame with an extremely large chunk_idx value, causing the scheduler to allocate memory until the system runs out and terminates the process.

SGLang through 0.5.19 in prefill/decode disaggregation mode with the Mooncake KV transfer backend fails to validate bootstrap_room values, allowing unbounded transfer state allocation. An unauthenticated attacker can reach the decode engine's POST /generate endpoint and submit arbitrary bootstrap_room values to exhaust prefill process memory until out-of-memory termination.

SGLang through 0.5.19 in prefill/decode disaggregation mode contains an unauthenticated PUT /route endpoint on the prefill bootstrap service that allows attackers to poison the KV transfer routing table. Attackers can supply arbitrary rank_ip and rank_port values to redirect decode workers to attacker-controlled endpoints, causing denial of service or disclosure of KV transfer metadata including session identifiers and tensor-parallel topology parameters.

SGLang contains a model weight exfiltration vulnerability when no API keys are configured. SGLang exposes two endpoints that allow a remote attacker to trigger distributed weight broadcasting using NCCL and then trigger data transfer, enabling exfiltration of all model weights.