CVE-2025-23310: Nvidia Triton Server Buffer Overflow Flaw

CVE-2025-23310 Overview

NVIDIA Triton Inference Server for Windows and Linux contains a critical stack buffer overflow vulnerability that can be triggered by specially crafted inputs. A successful exploit of this vulnerability might lead to remote code execution, denial of service, information disclosure, and data tampering. This vulnerability affects organizations deploying NVIDIA Triton Inference Server for AI/ML inference workloads across both Windows and Linux environments.

Critical Impact

This stack buffer overflow vulnerability enables remote attackers to potentially execute arbitrary code, cause denial of service, disclose sensitive information, or tamper with data on affected NVIDIA Triton Inference Server deployments without requiring authentication or user interaction.

Affected Products

• NVIDIA Triton Inference Server (all vulnerable versions)
• Linux Kernel (as deployment platform)
• Microsoft Windows (as deployment platform)

Discovery Timeline

• 2025-08-06 - CVE-2025-23310 published to NVD
• 2025-08-12 - Last updated in NVD database

Technical Details for CVE-2025-23310

Vulnerability Analysis

This vulnerability is classified as CWE-121 (Stack-based Buffer Overflow), which occurs when a program writes data beyond the boundaries of a stack buffer. In the context of NVIDIA Triton Inference Server, the vulnerability arises when the server processes specially crafted inputs that exceed expected buffer sizes on the stack.

The attack can be initiated remotely over the network without requiring any privileges or user interaction, making it particularly dangerous for internet-facing or internally exposed Triton Inference Server deployments. The vulnerability allows attackers to potentially overwrite critical stack data including return addresses, local variables, and saved registers.

Root Cause

The root cause of CVE-2025-23310 is improper bounds checking when processing user-supplied input data in NVIDIA Triton Inference Server. When the server receives malformed or oversized input payloads, it fails to properly validate the size of the data before copying it to a stack-allocated buffer, resulting in a classic stack buffer overflow condition.

This type of vulnerability typically occurs when:

• Fixed-size stack buffers are used to store variable-length input
• Input length validation is missing or insufficient
• Unsafe memory copy operations are used without proper size constraints

Attack Vector

The vulnerability is exploitable over the network, allowing remote attackers to send specially crafted requests to the Triton Inference Server. The attack requires no authentication and no user interaction, making it highly exploitable in environments where the server is accessible.

An attacker would craft malicious inference requests containing oversized or specially structured input data designed to overflow the vulnerable stack buffer. By carefully controlling the overflow data, an attacker could:

1. Overwrite the return address to redirect execution flow
2. Inject and execute arbitrary shellcode
3. Crash the service causing denial of service
4. Leak sensitive memory contents through controlled reads

For detailed technical information, refer to the NVIDIA Security Advisory.

Detection Methods for CVE-2025-23310

Indicators of Compromise

• Unusual crash patterns or segmentation faults in Triton Inference Server processes
• Anomalous network traffic patterns targeting Triton Inference Server endpoints with oversized payloads
• Unexpected memory access violations in server logs
• Signs of code execution from non-standard memory regions

Detection Strategies

• Monitor Triton Inference Server process behavior for signs of memory corruption or unexpected crashes
• Implement network-level detection rules for malformed or oversized inference requests
• Deploy endpoint detection and response (EDR) solutions capable of detecting stack buffer overflow exploitation attempts
• Enable application-level logging and monitor for parsing errors or buffer-related exceptions

Monitoring Recommendations

• Configure alerts for Triton Inference Server service restarts or unexpected terminations
• Monitor system logs for memory-related errors and ASLR bypass attempts
• Implement network traffic analysis to detect reconnaissance or exploitation attempts against inference endpoints
• Review audit logs for unauthorized access patterns to Triton Inference Server APIs

How to Mitigate CVE-2025-23310

Immediate Actions Required

• Update NVIDIA Triton Inference Server to the latest patched version as specified in the NVIDIA security advisory
• Restrict network access to Triton Inference Server to trusted sources only using firewall rules
• Implement network segmentation to isolate AI/ML inference infrastructure
• Enable stack protection mechanisms (ASLR, DEP/NX, stack canaries) on the host operating system

Patch Information

NVIDIA has released security updates to address this vulnerability. Administrators should consult the NVIDIA Security Advisory for specific patch versions and update instructions. Given the critical severity of this vulnerability, immediate patching is strongly recommended.

Workarounds

• Implement strict input validation at the network perimeter using a Web Application Firewall (WAF) or API gateway
• Limit request sizes and implement rate limiting on Triton Inference Server endpoints
• Deploy the server behind a reverse proxy that can inspect and sanitize incoming requests
• Consider temporarily disabling public network access until patches can be applied

# Example: Restrict Triton Inference Server access using iptables
# Allow only trusted network (example: 10.0.0.0/8) to access Triton ports
iptables -A INPUT -p tcp --dport 8000 -s 10.0.0.0/8 -j ACCEPT
iptables -A INPUT -p tcp --dport 8001 -s 10.0.0.0/8 -j ACCEPT
iptables -A INPUT -p tcp --dport 8002 -s 10.0.0.0/8 -j ACCEPT
iptables -A INPUT -p tcp --dport 8000 -j DROP
iptables -A INPUT -p tcp --dport 8001 -j DROP
iptables -A INPUT -p tcp --dport 8002 -j DROP