Tensorrt Llm
Monthly
NVIDIA TensorRT-LLM contains a vulnerability in the OpenAI-compatible inference API, where an attacker could cause allocation of GPU resources without limits or throttling. A successful exploit of this vulnerability might lead to denial of service.
NVIDIA TensorRT-LLM contains a vulnerability in the OpenAI-compatible inference API where an attacker could trigger a reachable assertion in the sampler thread. A successful exploit of this vulnerability might lead to denial of service.
NVIDIA TensorRT-LLM for any platform contains a vulnerability in the gRPC server chat API endpoint, where an attacker could cause CWE-20 by local attack. A successful exploit of this vulnerability might lead to denial of service.
NVIDIA TensorRT-LLM for Linux contains a vulnerability where an attacker could cause improper control of code generation. A successful exploit of this vulnerability might lead to code execution, data tampering, and information disclosure.
NVIDIA TensorRT-LLM for Linux contains a vulnerability where an attacker could cause missing authentication for a critical function. A successful exploit of this vulnerability might lead to code execution, data tampering, and information disclosure.
NVIDIA TensorRT-LLM for any platform contains a vulnerability in visual gen server, where an attacker could cause an unsafe deserialization by unauthorized zeroMQ deserialization. A successful exploit of this vulnerability might lead to code execution.
NVIDIA TensorRT-LLM for Linux contains a vulnerability in the multimodal media fetching functions, where a network-accessible attacker could cause server-side request forgery. A successful exploit of this vulnerability might lead to denial of service and information disclosure.
Missing authentication in NVIDIA TensorRT-LLM for Linux lets an attacker reach the disaggregated orchestrator's FastAPI server directly and read, write, or delete internal cluster state, resulting in information disclosure, data tampering, and denial of service. The flaw (CWE-306) affects the orchestration layer that coordinates disaggregated prefill/decode inference workers. No public exploit identified at time of analysis, and the CVSS 3.1 base score is 7.3 with a local attack vector despite the request-based nature of the issue.
Memory corruption in NVIDIA TensorRT-LLM allows an attacker with local access to trigger a write-what-where primitive (CWE-123), enabling arbitrary memory writes that can corrupt data, crash the inference service, or leak sensitive information. The flaw carries a CVSS 7.4 (High) score with a local attack vector and high attack complexity, and affects the TensorRT-LLM library used to build and serve optimized large-language-model inference on NVIDIA GPUs. There is no public exploit identified at time of analysis and the issue is not listed in CISA KEV.
Heap-based buffer overflow in NVIDIA TensorRT-LLM's tensor deserialization path lets an adjacent, unauthenticated attacker corrupt heap memory by supplying a crafted serialized tensor, potentially causing information disclosure, data tampering, or denial of service. All platforms running affected TensorRT-LLM versions are impacted. There is no public exploit identified at time of analysis and the flaw is not listed in CISA KEV; NVIDIA rates exploitation as high-complexity (AC:H).
Local privilege-context deserialization in NVIDIA TensorRT-LLM lets an attacker who already has same-user access to a host running the inference stack abuse its inter-process communication layer to trigger unsafe object deserialization (CWE-502), potentially yielding code execution, information disclosure, data tampering, and denial of service. The flaw is vendor-reported by NVIDIA and carries a CVSS 3.1 base of 7.8 (AV:L), meaning it is not remotely reachable but converts existing local access into full compromise of the model-serving process. There is no public exploit identified at time of analysis and it is not listed in CISA KEV.
Insecure deserialization in NVIDIA TensorRT-LLM for Linux lets a local, low-privileged attacker abuse a weakness in the restricted unpickler that handles model-weight loading, potentially achieving code execution, privilege escalation, data tampering, and information disclosure. The flaw (CWE-502, CVSS 8.4) affects the GPU LLM-inference library and stems from the restricted unpickler failing to fully constrain what can be deserialized from an untrusted model artifact. There is no public exploit identified at time of analysis and the CVE is not listed in CISA KEV.
NVIDIA TensorRT-LLM contains a vulnerability in the OpenAI-compatible inference API, where an attacker could cause allocation of GPU resources without limits or throttling. A successful exploit of this vulnerability might lead to denial of service.
NVIDIA TensorRT-LLM contains a vulnerability in the OpenAI-compatible inference API where an attacker could trigger a reachable assertion in the sampler thread. A successful exploit of this vulnerability might lead to denial of service.
NVIDIA TensorRT-LLM for any platform contains a vulnerability in the gRPC server chat API endpoint, where an attacker could cause CWE-20 by local attack. A successful exploit of this vulnerability might lead to denial of service.
NVIDIA TensorRT-LLM for Linux contains a vulnerability where an attacker could cause improper control of code generation. A successful exploit of this vulnerability might lead to code execution, data tampering, and information disclosure.
NVIDIA TensorRT-LLM for Linux contains a vulnerability where an attacker could cause missing authentication for a critical function. A successful exploit of this vulnerability might lead to code execution, data tampering, and information disclosure.
NVIDIA TensorRT-LLM for any platform contains a vulnerability in visual gen server, where an attacker could cause an unsafe deserialization by unauthorized zeroMQ deserialization. A successful exploit of this vulnerability might lead to code execution.
NVIDIA TensorRT-LLM for Linux contains a vulnerability in the multimodal media fetching functions, where a network-accessible attacker could cause server-side request forgery. A successful exploit of this vulnerability might lead to denial of service and information disclosure.
Missing authentication in NVIDIA TensorRT-LLM for Linux lets an attacker reach the disaggregated orchestrator's FastAPI server directly and read, write, or delete internal cluster state, resulting in information disclosure, data tampering, and denial of service. The flaw (CWE-306) affects the orchestration layer that coordinates disaggregated prefill/decode inference workers. No public exploit identified at time of analysis, and the CVSS 3.1 base score is 7.3 with a local attack vector despite the request-based nature of the issue.
Memory corruption in NVIDIA TensorRT-LLM allows an attacker with local access to trigger a write-what-where primitive (CWE-123), enabling arbitrary memory writes that can corrupt data, crash the inference service, or leak sensitive information. The flaw carries a CVSS 7.4 (High) score with a local attack vector and high attack complexity, and affects the TensorRT-LLM library used to build and serve optimized large-language-model inference on NVIDIA GPUs. There is no public exploit identified at time of analysis and the issue is not listed in CISA KEV.
Heap-based buffer overflow in NVIDIA TensorRT-LLM's tensor deserialization path lets an adjacent, unauthenticated attacker corrupt heap memory by supplying a crafted serialized tensor, potentially causing information disclosure, data tampering, or denial of service. All platforms running affected TensorRT-LLM versions are impacted. There is no public exploit identified at time of analysis and the flaw is not listed in CISA KEV; NVIDIA rates exploitation as high-complexity (AC:H).
Local privilege-context deserialization in NVIDIA TensorRT-LLM lets an attacker who already has same-user access to a host running the inference stack abuse its inter-process communication layer to trigger unsafe object deserialization (CWE-502), potentially yielding code execution, information disclosure, data tampering, and denial of service. The flaw is vendor-reported by NVIDIA and carries a CVSS 3.1 base of 7.8 (AV:L), meaning it is not remotely reachable but converts existing local access into full compromise of the model-serving process. There is no public exploit identified at time of analysis and it is not listed in CISA KEV.
Insecure deserialization in NVIDIA TensorRT-LLM for Linux lets a local, low-privileged attacker abuse a weakness in the restricted unpickler that handles model-weight loading, potentially achieving code execution, privilege escalation, data tampering, and information disclosure. The flaw (CWE-502, CVSS 8.4) affects the GPU LLM-inference library and stems from the restricted unpickler failing to fully constrain what can be deserialized from an untrusted model artifact. There is no public exploit identified at time of analysis and the CVE is not listed in CISA KEV.