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Remote code execution is achievable in llama.cpp's LLaMA-Android JNI wrapper (builds b1886 through b7445) by exploiting a race condition use-after-free between `bench_1model()` and `free_1context()`, which share the `llama_context` heap object without any synchronization primitive. An attacker who wins the race can perform a heap spray with a crafted fake vtable, overwriting the vtable pointer at offset +0x30 in `llama_batch_allocr` so that `clear()` dispatches to attacker-controlled memory, achieving code execution within the Android application process. No active exploitation is confirmed (not in CISA KEV), and no public exploit code has been identified at time of analysis, though the fully documented vtable offset and exploitation technique substantially lower the development barrier for capable researchers.
Integer overflow in the llama.cpp LLaMA-Android JNI wrapper (builds b1886 through b7445) allows heap corruption through a crafted n_seq_max parameter, enabling denial of service or arbitrary code execution within Android applications that load untrusted GGUF model files. The flaw exists in the new_1batch() function, which multiplies sizeof(llama_seq_id) by an attacker-controlled value without bounds checking, causing heap allocation to wrap and undersize the buffer. No public exploit has been identified at time of analysis and this CVE is not in CISA KEV; a patched release (b7446) is confirmed available from the upstream ggml-org repository.
Use-after-free in the llama-server component of llama.cpp (builds b7492 through b9060) lets remote unauthenticated attackers crash the server or potentially execute code by racing six tokenization endpoints (/tokenize, /detokenize, /infill, /apply-template, /rerank, /anthropic/count_tokens) that read ctx_server.vocab directly on HTTP worker threads instead of going through the task queue. The flaw is only reachable when the server is started with --sleep-idle-seconds, since idle-driven vocab teardown on the main thread is what frees the object mid-handler. No public exploit identified at time of analysis and it is not listed in CISA KEV; the CVSS 4.0 base score is 9.2 but real-world exploitation is constrained by a hard-to-win TOCTOU race.
Remote code execution in llama.cpp's llama-server (builds b7492 through b9060) allows unauthenticated attackers to run arbitrary code by racing the server's idle-sleep transition when the --sleep-idle-seconds feature is enabled. Concurrent worker threads dereference a freed vocab pointer that can be reclaimed with attacker-controlled data. No public exploit identified at time of analysis, and the flaw is not in CISA KEV; exploitation is gated by a non-default configuration and a tight timing window (CVSS 4.0 base 9.2, AT:P/AC:H).
Heap buffer overflow in llama.cpp's server KV cache state-restore path (builds b4882 through b9058) lets an attacker who can place a crafted state file in the configured slot_save_path corrupt heap memory and potentially achieve arbitrary code execution. The flaw lives in state_read_data(), which computes a write size from an attacker-influenced cell_count without integer-overflow checking, so a malicious slot file can write past the tensor buffer. No public exploit identified at time of analysis and it is not listed in CISA KEV; the only reference is a third-party security-patches repository, so exploitation currently appears theoretical rather than active.
Integer underflow and heap out-of-bounds read in llama.cpp's DRY sampler (builds b3978 through b9058) allows remote unauthenticated attackers to crash the inference server or corrupt token sampling probabilities by sending a single crafted HTTP request. Setting the `dry_allowed_length` parameter to INT32_MIN triggers arithmetic wrap-around in the sampler, causing it to read memory before the allocated heap buffer and producing a SIGSEGV that terminates service for all connected users. No public exploit code and no CISA KEV listing have been identified at time of analysis, but the trivially reproducible single-parameter trigger makes this a high-priority issue for any deployment exposing the llama.cpp HTTP API on a network.
Integer overflow in llama.cpp's llama_batch_init() function across builds b1283 through b9058 enables heap corruption via unchecked multiplicative arithmetic in malloc() size calculations - when computed allocation sizes wrap past INT32_MAX, the resulting under-sized heap buffer is overwritten by subsequent batch operations, creating a credible path to arbitrary code execution in the context of the host process. The vulnerability affects versions 0.4.0 through 0.17.1 and any application embedding the affected build range of the library. No public exploit code has been identified at time of analysis and the CVE is not listed in the CISA KEV catalog, but the heap corruption primitive paired with the RCE tag warrants expedited patching in production AI inference pipelines.
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.
Llama.cpp server endpoints fail to validate the n_discard parameter from JSON input, allowing negative values that trigger out-of-bounds memory writes when the context buffer fills. This memory corruption vulnerability affects LLM inference operations and can be exploited remotely without authentication to crash the service or achieve code execution; public exploit code exists and no patch is currently available.
Null pointer dereference in the llama.cpp LLaMA-Android JNI wrapper's bench_1model() function allows denial of service via a crafted model file. Builds b1886 through b7445 (packaged releases 0.9.0-0.17.1) are affected; the function skips null validation on the model context pointer before dereferencing it, so supplying a malformed or truncated GGUF model file triggers a SIGSEGV that terminates the Android application process. No public exploit has been identified at time of analysis and this is not listed in CISA KEV, but the attack path is mechanically simple for anyone able to deliver a crafted model to the device.
Out-of-bounds heap read in llama.cpp's recurrent memory state restore path (builds b5702 through b7653) allows an attacker with write access to the slot save directory to craft a malicious slot file that triggers heap data leakage - including pointer values - into server logs, effectively defeating ASLR protections and enabling multi-stage exploitation. No public exploit has been identified at time of analysis, and the vulnerability is not listed in the CISA KEV catalogue. An upstream fix is available as a commit to the ggml-org/llama.cpp repository, though a tagged released version past the affected build range has not been independently confirmed.
CVE-2025-52566 is a signed vs. unsigned integer overflow vulnerability in llama.cpp's tokenizer (llama_vocab::tokenize function) that enables heap buffer overflow during text tokenization. This affects all versions of llama.cpp prior to b5721, and attackers can trigger the vulnerability with specially crafted text input during the inference process, potentially achieving code execution with high confidentiality, integrity, and availability impact. The vulnerability requires local access and user interaction but has a high CVSS score of 8.6; KEV status and active exploitation data are not currently available, but the patch exists in version b5721.
A buffer overflow vulnerability in llama.cpp (CVSS 8.8). High severity vulnerability requiring prompt remediation. Vendor patch is available.
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.
llama.cpp provides LLM inference in C/C++. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
llama.cpp provides LLM inference in C/C++. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. This Out-of-bounds Read vulnerability could allow attackers to read data from memory outside the intended buffer boundaries.
llama.cpp provides LLM inference in C/C++. Rated medium severity (CVSS 6.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. This NULL Pointer Dereference vulnerability could allow attackers to crash the application by dereferencing a null pointer.
Llama.cpp is LLM inference in C/C++. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. No vendor patch available.
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.
Remote code execution is achievable in llama.cpp's LLaMA-Android JNI wrapper (builds b1886 through b7445) by exploiting a race condition use-after-free between `bench_1model()` and `free_1context()`, which share the `llama_context` heap object without any synchronization primitive. An attacker who wins the race can perform a heap spray with a crafted fake vtable, overwriting the vtable pointer at offset +0x30 in `llama_batch_allocr` so that `clear()` dispatches to attacker-controlled memory, achieving code execution within the Android application process. No active exploitation is confirmed (not in CISA KEV), and no public exploit code has been identified at time of analysis, though the fully documented vtable offset and exploitation technique substantially lower the development barrier for capable researchers.
Integer overflow in the llama.cpp LLaMA-Android JNI wrapper (builds b1886 through b7445) allows heap corruption through a crafted n_seq_max parameter, enabling denial of service or arbitrary code execution within Android applications that load untrusted GGUF model files. The flaw exists in the new_1batch() function, which multiplies sizeof(llama_seq_id) by an attacker-controlled value without bounds checking, causing heap allocation to wrap and undersize the buffer. No public exploit has been identified at time of analysis and this CVE is not in CISA KEV; a patched release (b7446) is confirmed available from the upstream ggml-org repository.
Use-after-free in the llama-server component of llama.cpp (builds b7492 through b9060) lets remote unauthenticated attackers crash the server or potentially execute code by racing six tokenization endpoints (/tokenize, /detokenize, /infill, /apply-template, /rerank, /anthropic/count_tokens) that read ctx_server.vocab directly on HTTP worker threads instead of going through the task queue. The flaw is only reachable when the server is started with --sleep-idle-seconds, since idle-driven vocab teardown on the main thread is what frees the object mid-handler. No public exploit identified at time of analysis and it is not listed in CISA KEV; the CVSS 4.0 base score is 9.2 but real-world exploitation is constrained by a hard-to-win TOCTOU race.
Remote code execution in llama.cpp's llama-server (builds b7492 through b9060) allows unauthenticated attackers to run arbitrary code by racing the server's idle-sleep transition when the --sleep-idle-seconds feature is enabled. Concurrent worker threads dereference a freed vocab pointer that can be reclaimed with attacker-controlled data. No public exploit identified at time of analysis, and the flaw is not in CISA KEV; exploitation is gated by a non-default configuration and a tight timing window (CVSS 4.0 base 9.2, AT:P/AC:H).
Heap buffer overflow in llama.cpp's server KV cache state-restore path (builds b4882 through b9058) lets an attacker who can place a crafted state file in the configured slot_save_path corrupt heap memory and potentially achieve arbitrary code execution. The flaw lives in state_read_data(), which computes a write size from an attacker-influenced cell_count without integer-overflow checking, so a malicious slot file can write past the tensor buffer. No public exploit identified at time of analysis and it is not listed in CISA KEV; the only reference is a third-party security-patches repository, so exploitation currently appears theoretical rather than active.
Integer underflow and heap out-of-bounds read in llama.cpp's DRY sampler (builds b3978 through b9058) allows remote unauthenticated attackers to crash the inference server or corrupt token sampling probabilities by sending a single crafted HTTP request. Setting the `dry_allowed_length` parameter to INT32_MIN triggers arithmetic wrap-around in the sampler, causing it to read memory before the allocated heap buffer and producing a SIGSEGV that terminates service for all connected users. No public exploit code and no CISA KEV listing have been identified at time of analysis, but the trivially reproducible single-parameter trigger makes this a high-priority issue for any deployment exposing the llama.cpp HTTP API on a network.
Integer overflow in llama.cpp's llama_batch_init() function across builds b1283 through b9058 enables heap corruption via unchecked multiplicative arithmetic in malloc() size calculations - when computed allocation sizes wrap past INT32_MAX, the resulting under-sized heap buffer is overwritten by subsequent batch operations, creating a credible path to arbitrary code execution in the context of the host process. The vulnerability affects versions 0.4.0 through 0.17.1 and any application embedding the affected build range of the library. No public exploit code has been identified at time of analysis and the CVE is not listed in the CISA KEV catalog, but the heap corruption primitive paired with the RCE tag warrants expedited patching in production AI inference pipelines.
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.
Llama.cpp server endpoints fail to validate the n_discard parameter from JSON input, allowing negative values that trigger out-of-bounds memory writes when the context buffer fills. This memory corruption vulnerability affects LLM inference operations and can be exploited remotely without authentication to crash the service or achieve code execution; public exploit code exists and no patch is currently available.
Null pointer dereference in the llama.cpp LLaMA-Android JNI wrapper's bench_1model() function allows denial of service via a crafted model file. Builds b1886 through b7445 (packaged releases 0.9.0-0.17.1) are affected; the function skips null validation on the model context pointer before dereferencing it, so supplying a malformed or truncated GGUF model file triggers a SIGSEGV that terminates the Android application process. No public exploit has been identified at time of analysis and this is not listed in CISA KEV, but the attack path is mechanically simple for anyone able to deliver a crafted model to the device.
Out-of-bounds heap read in llama.cpp's recurrent memory state restore path (builds b5702 through b7653) allows an attacker with write access to the slot save directory to craft a malicious slot file that triggers heap data leakage - including pointer values - into server logs, effectively defeating ASLR protections and enabling multi-stage exploitation. No public exploit has been identified at time of analysis, and the vulnerability is not listed in the CISA KEV catalogue. An upstream fix is available as a commit to the ggml-org/llama.cpp repository, though a tagged released version past the affected build range has not been independently confirmed.
CVE-2025-52566 is a signed vs. unsigned integer overflow vulnerability in llama.cpp's tokenizer (llama_vocab::tokenize function) that enables heap buffer overflow during text tokenization. This affects all versions of llama.cpp prior to b5721, and attackers can trigger the vulnerability with specially crafted text input during the inference process, potentially achieving code execution with high confidentiality, integrity, and availability impact. The vulnerability requires local access and user interaction but has a high CVSS score of 8.6; KEV status and active exploitation data are not currently available, but the patch exists in version b5721.
A buffer overflow vulnerability in llama.cpp (CVSS 8.8). High severity vulnerability requiring prompt remediation. Vendor patch is available.
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.
llama.cpp provides LLM inference in C/C++. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
llama.cpp provides LLM inference in C/C++. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. This Out-of-bounds Read vulnerability could allow attackers to read data from memory outside the intended buffer boundaries.
llama.cpp provides LLM inference in C/C++. Rated medium severity (CVSS 6.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. This NULL Pointer Dereference vulnerability could allow attackers to crash the application by dereferencing a null pointer.
Llama.cpp is LLM inference in C/C++. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. No vendor patch available.
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.