CVE-2026-86317 in llama.cpp
Summary
by MITRE • 09/07/2026
A vulnerability was detected in ggml-org llama.cpp up to 0.4.0. This impacts the function rpc_server::deserialize_tensor of the file ggml/src/ggml-rpc/ggml-rpc.cpp of the component RPC Server. Performing a manipulation of the argument ne results in reachable assertion. The attack is possible to be carried out remotely. The reported GitHub issue was closed automatically due to inactivity.
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Analysis
by VulDB Data Team • 09/07/2026
The vulnerability identified within ggml-org llama.cpp versions up to 0.4.0 represents a critical security flaw located specifically within the RPC server component, which facilitates remote procedure calls for distributed inference tasks. This architectural feature allows multiple devices or processes to collaborate on large language model computations by sharing tensor data over a network interface. The core of the vulnerability resides in the rpc_server::deserialize_tensor function found in the ggml/src/ggml-rpc/ggml-rpc.cpp source file. During the deserialization process, which is responsible for reconstructing tensor objects from incoming serialized byte streams received via RPC connections, the system fails to adequately validate or sanitize specific structural arguments before processing them. This lack of rigorous input validation creates a pathway for remote attackers to exploit the internal logic of the server by manipulating critical metadata fields associated with the tensor structure.
The technical mechanism of this exploitation centers on the manipulation of the ne argument, which typically denotes the number of elements in a dimension of the tensor being transmitted. When an attacker crafts a malicious RPC request containing a specifically crafted value for this parameter, it triggers an internal assertion failure within the llama.cpp codebase rather than handling the error gracefully or returning a controlled exception. Assertions are generally intended as debugging aids that assume certain conditions will always be true during normal operation; however, when these assumptions are violated by external input without proper boundary checks, they result in immediate program termination via abort signals. This behavior classifies the vulnerability primarily under CWE-617: Reachable Assertion, indicating that an attacker can force the application to execute a code path leading to its own crash through valid but maliciously constructed inputs.
From an operational perspective, this flaw poses a significant risk of denial-of-service against any service relying on llama.cpp for remote inference capabilities. Because the attack vector is remotely exploitable, an adversary does not need local access or authentication credentials if the RPC server is exposed to untrusted networks without additional security controls such as firewalls or mutual TLS. The immediate consequence of triggering this assertion failure is a crash of the rpc_server process, leading to service disruption for all connected clients attempting to utilize the distributed computing resources. In environments where high availability and continuous inference are required, such instability can cause cascading failures across dependent systems that rely on consistent response times from the model serving infrastructure.
This vulnerability aligns with broader attack patterns documented in industry frameworks. It corresponds to CWE-20: Improper Input Validation, as the root cause is the failure to verify the integrity and range of incoming data before processing. Furthermore, within the MITRE ATT&CK framework, this behavior can be associated with T1499: Endpoint Denial of Service, specifically under techniques that involve resource exhaustion or application crashes through malformed inputs. The fact that the reported GitHub issue was closed automatically due to inactivity highlights a potential gap in community-driven maintenance cycles for open-source security projects, underscoring the importance of proactive vulnerability scanning and timely patching strategies for organizations deploying these libraries in production environments.
To mitigate this risk, immediate action should be taken by upgrading llama.cpp to version 0.4.1 or later where such assertion flaws have typically been addressed with proper error handling mechanisms instead of fatal crashes. If an upgrade is not immediately feasible, network-level mitigations are strongly recommended. This includes restricting access to the RPC server ports using firewall rules to allow only trusted IP addresses and implementing mutual TLS authentication to ensure that only authorized clients can initiate tensor deserialization requests. Additionally, deploying intrusion detection systems capable of identifying anomalous packet sizes or malformed serialization structures in real-time can provide an additional layer of defense against exploitation attempts targeting this specific function within the RPC server component.