Llama Cpp

Remote code execution in llama.cpp RPC backend allows unauthenticated attackers with TCP access to achieve arbitrary memory read/write and full ASLR bypass. The vulnerability stems from missing bounds validation in deserialize_tensor() when processing GRAPH_COMPUTE messages with zero-valued buffer fields. Attackers can leverage pointer leaks from ALLOC_BUFFER/BUFFER_GET_BASE operations to reliably exploit this flaw. Fixed in version b8492 (commit 39bf0d3c). CVSS 9.8 (Critical) with network attack vector, low complexity, and no authentication required. No public exploit identified at time of analysis, though the detailed advisory provides sufficient technical context for weaponization.

Remote code execution in llama.cpp prior to commit b7824 is possible through a crafted GGUF file that exploits an integer overflow in the `ggml_nbytes` function, causing heap buffer overflow during tensor processing. An attacker can bypass memory validation by specifying tensor dimensions that cause the size calculation to underflow dramatically, allowing memory corruption and potential code execution. The vulnerability affects Debian and other systems running vulnerable versions of llama.cpp, with no patch currently available.

Local attackers can achieve heap buffer overflow in llama.cpp versions before b8146 through integer overflow in the GGUF file parsing function, enabling arbitrary code execution with high integrity and confidentiality impact. The vulnerability stems from undersized heap allocation followed by unvalidated writes of over 528 bytes of attacker-controlled data, bypassing a previous fix for the same component. This affects systems running vulnerable LLM inference implementations on local machines where user interaction is required to trigger the malicious GGUF file processing.

Arbitrary memory write in llama.cpp's RPC server allows remote unauthenticated attackers to corrupt arbitrary memory addresses via the unsafe `data` pointer in the `rpc_tensor` structure, leading to full code execution on the host running the inference service. The flaw earned a maximum CVSS 10.0 (scope changed) and publicly available exploit code exists, though it is not yet listed in CISA KEV; EPSS sits at 5.68% (90th percentile), reflecting elevated but not widespread targeting. Fixed in release b3561.

Remote code execution in llama.cpp (GGUF library) allows attackers to achieve arbitrary code execution by tricking a user into loading a maliciously crafted .gguf model file, exploiting a heap-based buffer overflow in the header.n_kv parsing logic at commit 18c2e17. Publicly available exploit code exists, though EPSS rates real-world exploitation probability low at 0.15% (35th percentile), reflecting the user-interaction requirement. The flaw was reported by Cisco Talos and impacts confidentiality, integrity, and availability of any system loading untrusted GGUF models.

Remote code execution in llama.cpp (commit 18c2e17) occurs when the GGUF library's gguf_fread_str function parses a maliciously crafted .gguf model file, triggering a heap-based buffer overflow rooted in integer overflow handling (CWE-190). Any user or service loading an untrusted GGUF model into a vulnerable llama.cpp build can be compromised, with publicly available exploit code increasing accessibility despite a low EPSS score of 0.15%.

Heap-based buffer overflow in llama.cpp's GGUF library header parser (commit 18c2e17) enables code execution when a victim loads a maliciously crafted .gguf model file. The CWE-190 integer overflow in the n_tensors field corrupts heap allocations, leading to attacker-controlled memory writes. Publicly available exploit code exists, though EPSS remains low at 0.15% (35th percentile), and there is no public exploit identified as actively used per CISA KEV.

Remote code execution in llama.cpp (commit 18c2e17) is possible when a victim loads a malicious .gguf model file, triggering a heap-based buffer overflow in the GGUF library's GGUF_TYPE_ARRAY/GGUF_TYPE_STRING parsing routines. Publicly available exploit code exists, though EPSS rates near-term mass exploitation probability as low (0.19%, 41st percentile) and the issue is not listed in CISA KEV.

Remote code execution in llama.cpp (commit 18c2e17) is possible when a user opens a malicious .gguf model file, triggering a heap-based buffer overflow in the GGUF library's info->ne handling. Publicly available exploit code exists, though EPSS estimates exploitation probability at 0.48% (65th percentile), reflecting moderate but not widespread targeting risk against this AI inference runtime.