Ggml Org
Monthly
Unauthenticated remote attackers can terminate the llama.cpp ggml-rpc rpc-server process - and with it every distributed-inference session it is serving - by sending a command whose declared message length exceeds std::vector::max_size() once the protocol HELLO handshake completes. Builds from b2877 through b11934 are affected: the exception thrown while sizing the receive buffer is a std::length_error, which the server's handler did not catch, so the process aborts instead of rejecting the frame. No public exploit code was identified at time of analysis, and reachability is bounded by deployment - rpc-server is an optional, non-default backend, so only installations that explicitly enable and expose it on the network are at risk. Availability-only impact; no confidentiality or integrity effect.
Stack-based out-of-bounds write in llama.cpp's ggml-rpc RDMA receive path (builds b8841 through b11865) lets unauthenticated network peers corrupt the rpc-server stack by advertising RDMA support in the HELLO message and sending up to 256 KiB into a 1-byte stack variable, crashing the server or potentially enabling code execution. The input CVSS 4.0 score is 9.2 Critical and the vuln.today decision is act_now, but exploitation requires the opt-in llama.cpp rpc-server to be running with the non-default RDMA transport and reachable from the attacker, which is reflected in the independent CVSS 3.1 assessment as AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H. No public exploit was identified in the provided intelligence at time of analysis, and the vendor fix is available in build b11865.
Heap out-of-bounds write in llama.cpp builds b5899 through b10260 lets an attacker corrupt heap memory when the victim loads a crafted GGUF model that uses the PLaMo2 tokenizer. A BYTE-typed token whose text parses to a negative value (for example <0x-1>) is used as an index into the tokenizer's bytes_ buffer during model loading, so the write lands before the buffer and can crash the process or potentially enable code execution. The flaw is local in nature (it triggers only when an untrusted model is loaded, per the assessed CVSS:3.1/AV:L/AC:L/PR:N/UI:R vector), a vendor patch is available in build b10261, no public exploit has been identified at time of analysis, and EPSS is low at 0.14% (3rd percentile) with a this_week priority label.
Heap memory corruption in llama.cpp's chat/tool parser lets unauthenticated remote attackers crash llama-server and potentially shape a controlled heap write primitive by sending a crafted POST /completion request containing a chat_parser that emits a tool-id token after a tool-close tag. Versions prior to build b11393 are affected; the trigger leaves the mapper's current_tool pointer dangling, producing a use-after-free and double-free in common_chat_peg_mapper::map. No public exploit code was identified at time of analysis and the flaw is not in CISA KEV, but the CVSS 4.0 score of 9.2 reflects high confidentiality, integrity and availability impact, and the independent assessment confirms high severity (CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H) - reliable denial of service is straightforward, while converting the bug into a controlled heap write requires heap grooming. A vendor patch exists as upstream PR/commit and as build b11393.
Heap corruption via mismatched memory management in llama.cpp's LLaMA-Android JNI wrapper affects builds b1886 through b7445, where new_1batch() allocates memory with malloc() but free_1batch() releases it using the C++ delete operator, triggering undefined behavior and heap metadata corruption. This mismatch guarantees denial of service via process crash and, depending on allocator state, may enable arbitrary code execution within the affected Android application process. No public exploit code has been confirmed and the vulnerability is not listed in CISA KEV; a vendor-released patch exists as build b7446.
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.
Out-of-bounds memory access in llama.cpp builds b10398 through b11300 arises because a crafted GGUF model file can declare tensor dimensions whose product overflows INT64_MAX and wraps to zero, slipping past the element-count validation in gguf_init_from_reader so that ggml_nbytes() and stride calculations size buffers far too small. The flaw is only reachable when a vulnerable build loads and parses an attacker-supplied model: the assessed vector is local (AV:L) with user interaction required (UI:R), so there is no remote, network-triggered or unauthenticated path, and risk is concentrated in workflows that ingest third-party GGUF artifacts such as model hubs, shared storage, or services accepting user-uploaded models. Exploitation yields limited confidentiality and integrity impact with a high availability impact, but no public exploit code has been identified at time of analysis, EPSS is only 0.13% (2nd percentile), and the issue is not in CISA KEV; a vendor patch is available in b11301 and the prioritization signal is this_month rather than act_now.
A heap out-of-bounds read in llama.cpp's vocabulary loader (llama_vocab::impl::print_info) lets a crafted GGUF model crash llama-cli or llama-server during model load; it affects builds prior to b5649. Unauthenticated remote attackers cannot trigger it on their own - the operator must be induced to load an attacker-supplied model (CVSSv4 UI:P, assessed CVSSv3.1 AV:L/UI:R) - and the only demonstrated impact is process termination, with no confidentiality or integrity effect and no code execution shown. A public PoC reference exists; it is not verified as working exploit code. No KEV listing, EPSS 0.12% (2nd percentile), and the vendor fix is available as build b5649, so this is a genuinely medium-tier issue that should not be treated as an emergency and is scheduled for this month's patch cycle.
Reachable assertion in llama.cpp (through 0.7.0) lets unauthenticated remote callers abort the inference process by driving the non-default Mirostat sampler into non-finite or zero-sum token probabilities, producing a denial of service that is confined to availability. Only deployments that expose an inference endpoint such as llama-server to untrusted clients - and that permit those clients to select Mirostat v1/v2 and influence sampling parameters or inputs that make probabilities non-finite - are affected; there is no code execution, data disclosure or integrity impact. No public exploit code and no CISA KEV entry were identified at time of analysis, and the issue carries a moderate this_month priority rather than urgent handling. The flaw was reported upstream through pull request #29992, which adds the missing guards; a tagged release containing that fix is not independently confirmed.
An out-of-bounds read in ggml-org whisper.cpp through version 1.9.4 lets a local attacker crash the application by getting a victim to load a crafted GGML model file whose tensor-type field ('ttype') falls outside the valid range. Exploitation is strictly local and requires user interaction - the victim must choose to open the untrusted model - and the assessed impact is limited to availability (a process crash), with no confidentiality or integrity effect. Publicly available exploit code exists (the upstream issue and PR are public), but the vulnerability is not in CISA KEV and its severity is low.
Unauthenticated remote denial-of-service in llama.cpp's RPC server allows a remote attacker to crash the server process by sending a crafted tensor descriptor with a manipulated `ne` (number-of-elements) argument to `rpc_server::deserialize_tensor`, triggering a reachable assertion (CWE-617) and aborting the process. Affected versions span llama.cpp up to 0.4.0 when the optional RPC server component is compiled and active. No patch has been released; the upstream GitHub issue was closed automatically due to inactivity, and no public exploit or CISA KEV listing exists at time of analysis.
Null pointer dereference in llama.cpp's ggml-RPC server (commit bec4772f6) allows remote unauthenticated denial of service against any deployment exposing the RPC port to the network. The flaw resides in rpc_server::graph_compute, where a graph node with id=0 bypasses a conditional null check, enabling a single crafted TCP packet to crash the server process. No standalone exploit code is publicly available at time of analysis, though the upstream fix PR includes a Python test script that reproducibly triggers the crash in under one second.
Reachable assertion in llama.cpp's Jinja Minja Template Parser crashes the process when processing a maliciously crafted template input. The flaw, located in common/jinja/parser.cpp, is triggered locally by a low-privileged user supplying the input sequence {{9|9|{ to the template engine, causing an unhandled assertion failure and denial of service. A public proof-of-concept exploit has been disclosed; the project was notified via GitHub issue #25282 but has not issued a patch or response as of this writing.
Local denial of service in ggml-org whisper.cpp 95ea8f9b via reachable assertion in ggml_ftype_to_ggml_type function. A local attacker with low privileges can cause a crash by supplying an invalid ftype argument. No public exploit identified; no patch available as vendor has not responded to issue report.
Local attackers with low privileges can trigger an out-of-bounds read in whisper.cpp 1.8.4-58 by supplying a very short audio sample to the log_mel_spectrogram function. This heap read overflow may leak sensitive information but does not allow code execution or system compromise. No public exploit or active exploitation has been reported.
Remote denial of service in llama.cpp allows unauthenticated attackers to exhaust server resources via crafted JSON schema inputs that trigger unbounded recursion in the JSON-schema-to-GBNF conversion routine. Affected versions include commit e15efe0 and prior. A fix exists in a pending pull request; no active exploitation reported.
Denial of service in ggml-org llama.cpp allows remote attackers to crash the application by sending a crafted JSON schema that triggers a null pointer dereference in the _visit_pattern function. No authentication or user interaction is required, and all versions up to the affected commits d006858/e15efe0 are vulnerable. The fix is pending merge; no public exploit code has been identified.
Null pointer dereference in whisper.cpp up to version 1.8.2 allows a local authenticated attacker to crash the application via a crafted input during model loading. The vulnerable code path is the whisper_model_load function within ggml/src/ggml.c, resulting in a limited availability impact (application denial of service) with no confidentiality or integrity consequences. A proof-of-concept exploit is publicly available via a GitHub issue report; however, no vendor patch has been released and the project has not yet responded to the disclosure.
Unauthenticated remote attackers can terminate the llama.cpp ggml-rpc rpc-server process - and with it every distributed-inference session it is serving - by sending a command whose declared message length exceeds std::vector::max_size() once the protocol HELLO handshake completes. Builds from b2877 through b11934 are affected: the exception thrown while sizing the receive buffer is a std::length_error, which the server's handler did not catch, so the process aborts instead of rejecting the frame. No public exploit code was identified at time of analysis, and reachability is bounded by deployment - rpc-server is an optional, non-default backend, so only installations that explicitly enable and expose it on the network are at risk. Availability-only impact; no confidentiality or integrity effect.
Stack-based out-of-bounds write in llama.cpp's ggml-rpc RDMA receive path (builds b8841 through b11865) lets unauthenticated network peers corrupt the rpc-server stack by advertising RDMA support in the HELLO message and sending up to 256 KiB into a 1-byte stack variable, crashing the server or potentially enabling code execution. The input CVSS 4.0 score is 9.2 Critical and the vuln.today decision is act_now, but exploitation requires the opt-in llama.cpp rpc-server to be running with the non-default RDMA transport and reachable from the attacker, which is reflected in the independent CVSS 3.1 assessment as AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H. No public exploit was identified in the provided intelligence at time of analysis, and the vendor fix is available in build b11865.
Heap out-of-bounds write in llama.cpp builds b5899 through b10260 lets an attacker corrupt heap memory when the victim loads a crafted GGUF model that uses the PLaMo2 tokenizer. A BYTE-typed token whose text parses to a negative value (for example <0x-1>) is used as an index into the tokenizer's bytes_ buffer during model loading, so the write lands before the buffer and can crash the process or potentially enable code execution. The flaw is local in nature (it triggers only when an untrusted model is loaded, per the assessed CVSS:3.1/AV:L/AC:L/PR:N/UI:R vector), a vendor patch is available in build b10261, no public exploit has been identified at time of analysis, and EPSS is low at 0.14% (3rd percentile) with a this_week priority label.
Heap memory corruption in llama.cpp's chat/tool parser lets unauthenticated remote attackers crash llama-server and potentially shape a controlled heap write primitive by sending a crafted POST /completion request containing a chat_parser that emits a tool-id token after a tool-close tag. Versions prior to build b11393 are affected; the trigger leaves the mapper's current_tool pointer dangling, producing a use-after-free and double-free in common_chat_peg_mapper::map. No public exploit code was identified at time of analysis and the flaw is not in CISA KEV, but the CVSS 4.0 score of 9.2 reflects high confidentiality, integrity and availability impact, and the independent assessment confirms high severity (CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H) - reliable denial of service is straightforward, while converting the bug into a controlled heap write requires heap grooming. A vendor patch exists as upstream PR/commit and as build b11393.
Heap corruption via mismatched memory management in llama.cpp's LLaMA-Android JNI wrapper affects builds b1886 through b7445, where new_1batch() allocates memory with malloc() but free_1batch() releases it using the C++ delete operator, triggering undefined behavior and heap metadata corruption. This mismatch guarantees denial of service via process crash and, depending on allocator state, may enable arbitrary code execution within the affected Android application process. No public exploit code has been confirmed and the vulnerability is not listed in CISA KEV; a vendor-released patch exists as build b7446.
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.
Out-of-bounds memory access in llama.cpp builds b10398 through b11300 arises because a crafted GGUF model file can declare tensor dimensions whose product overflows INT64_MAX and wraps to zero, slipping past the element-count validation in gguf_init_from_reader so that ggml_nbytes() and stride calculations size buffers far too small. The flaw is only reachable when a vulnerable build loads and parses an attacker-supplied model: the assessed vector is local (AV:L) with user interaction required (UI:R), so there is no remote, network-triggered or unauthenticated path, and risk is concentrated in workflows that ingest third-party GGUF artifacts such as model hubs, shared storage, or services accepting user-uploaded models. Exploitation yields limited confidentiality and integrity impact with a high availability impact, but no public exploit code has been identified at time of analysis, EPSS is only 0.13% (2nd percentile), and the issue is not in CISA KEV; a vendor patch is available in b11301 and the prioritization signal is this_month rather than act_now.
A heap out-of-bounds read in llama.cpp's vocabulary loader (llama_vocab::impl::print_info) lets a crafted GGUF model crash llama-cli or llama-server during model load; it affects builds prior to b5649. Unauthenticated remote attackers cannot trigger it on their own - the operator must be induced to load an attacker-supplied model (CVSSv4 UI:P, assessed CVSSv3.1 AV:L/UI:R) - and the only demonstrated impact is process termination, with no confidentiality or integrity effect and no code execution shown. A public PoC reference exists; it is not verified as working exploit code. No KEV listing, EPSS 0.12% (2nd percentile), and the vendor fix is available as build b5649, so this is a genuinely medium-tier issue that should not be treated as an emergency and is scheduled for this month's patch cycle.
Reachable assertion in llama.cpp (through 0.7.0) lets unauthenticated remote callers abort the inference process by driving the non-default Mirostat sampler into non-finite or zero-sum token probabilities, producing a denial of service that is confined to availability. Only deployments that expose an inference endpoint such as llama-server to untrusted clients - and that permit those clients to select Mirostat v1/v2 and influence sampling parameters or inputs that make probabilities non-finite - are affected; there is no code execution, data disclosure or integrity impact. No public exploit code and no CISA KEV entry were identified at time of analysis, and the issue carries a moderate this_month priority rather than urgent handling. The flaw was reported upstream through pull request #29992, which adds the missing guards; a tagged release containing that fix is not independently confirmed.
An out-of-bounds read in ggml-org whisper.cpp through version 1.9.4 lets a local attacker crash the application by getting a victim to load a crafted GGML model file whose tensor-type field ('ttype') falls outside the valid range. Exploitation is strictly local and requires user interaction - the victim must choose to open the untrusted model - and the assessed impact is limited to availability (a process crash), with no confidentiality or integrity effect. Publicly available exploit code exists (the upstream issue and PR are public), but the vulnerability is not in CISA KEV and its severity is low.
Unauthenticated remote denial-of-service in llama.cpp's RPC server allows a remote attacker to crash the server process by sending a crafted tensor descriptor with a manipulated `ne` (number-of-elements) argument to `rpc_server::deserialize_tensor`, triggering a reachable assertion (CWE-617) and aborting the process. Affected versions span llama.cpp up to 0.4.0 when the optional RPC server component is compiled and active. No patch has been released; the upstream GitHub issue was closed automatically due to inactivity, and no public exploit or CISA KEV listing exists at time of analysis.
Null pointer dereference in llama.cpp's ggml-RPC server (commit bec4772f6) allows remote unauthenticated denial of service against any deployment exposing the RPC port to the network. The flaw resides in rpc_server::graph_compute, where a graph node with id=0 bypasses a conditional null check, enabling a single crafted TCP packet to crash the server process. No standalone exploit code is publicly available at time of analysis, though the upstream fix PR includes a Python test script that reproducibly triggers the crash in under one second.
Reachable assertion in llama.cpp's Jinja Minja Template Parser crashes the process when processing a maliciously crafted template input. The flaw, located in common/jinja/parser.cpp, is triggered locally by a low-privileged user supplying the input sequence {{9|9|{ to the template engine, causing an unhandled assertion failure and denial of service. A public proof-of-concept exploit has been disclosed; the project was notified via GitHub issue #25282 but has not issued a patch or response as of this writing.
Local denial of service in ggml-org whisper.cpp 95ea8f9b via reachable assertion in ggml_ftype_to_ggml_type function. A local attacker with low privileges can cause a crash by supplying an invalid ftype argument. No public exploit identified; no patch available as vendor has not responded to issue report.
Local attackers with low privileges can trigger an out-of-bounds read in whisper.cpp 1.8.4-58 by supplying a very short audio sample to the log_mel_spectrogram function. This heap read overflow may leak sensitive information but does not allow code execution or system compromise. No public exploit or active exploitation has been reported.
Remote denial of service in llama.cpp allows unauthenticated attackers to exhaust server resources via crafted JSON schema inputs that trigger unbounded recursion in the JSON-schema-to-GBNF conversion routine. Affected versions include commit e15efe0 and prior. A fix exists in a pending pull request; no active exploitation reported.
Denial of service in ggml-org llama.cpp allows remote attackers to crash the application by sending a crafted JSON schema that triggers a null pointer dereference in the _visit_pattern function. No authentication or user interaction is required, and all versions up to the affected commits d006858/e15efe0 are vulnerable. The fix is pending merge; no public exploit code has been identified.
Null pointer dereference in whisper.cpp up to version 1.8.2 allows a local authenticated attacker to crash the application via a crafted input during model loading. The vulnerable code path is the whisper_model_load function within ggml/src/ggml.c, resulting in a limited availability impact (application denial of service) with no confidentiality or integrity consequences. A proof-of-concept exploit is publicly available via a GitHub issue report; however, no vendor patch has been released and the project has not yet responded to the disclosure.