Author: Ameeba

Overview

The NVIDIA Triton Inference Server, a popular platform for deploying AI models, is susceptible to a critical vulnerability, CVE-2025-23331. This vulnerability affects both Windows and Linux versions of the server and could potentially lead to a system compromise or data leakage. The vulnerability enables a user to trigger a memory allocation with an excessively large size value, causing a segmentation fault by providing an invalid request.

Vulnerability Summary

CVE ID: CVE-2025-23331
Severity: Critical (7.5 CVSS Score)
Attack Vector: Network
Privileges Required: Low
User Interaction: None
Impact: Denial of service, potential system compromise, and data leakage

Affected Products

Product | Affected Versions

NVIDIA Triton Inference Server for Windows | All Versions
NVIDIA Triton Inference Server for Linux | All Versions

How the Exploit Works

The exploit takes advantage of the server’s failure to validate and properly handle the size value of a user’s request. By providing an invalid request with an excessively large size value, the user can trigger a segmentation fault. This fault can lead to a denial of service and, in certain circumstances, allow for further exploitation that could result in system compromise or data leakage.

Conceptual Example Code

Below is a conceptual example of how the vulnerability might be exploited. This is a sample HTTP request with a malicious payload designed to trigger a segmentation fault.

POST /api/v1/inference HTTP/1.1
Host: target.example.com
Content-Type: application/json
{ "data_size": "99999999999999999999999999999", "data": "malicious_data" }

Mitigation Guidance

Users are strongly advised to apply the vendor patch as soon as it becomes available. Until then, the use of a Web Application Firewall (WAF) or Intrusion Detection System (IDS) can serve as a temporary mitigation measure.

Overview

A critical vulnerability, designated as CVE-2025-23327, has been identified in NVIDIA’s Triton Inference Server for Windows and Linux. This vulnerability can lead to an integer overflow if exploited, potentially causing denial of service and data tampering. This issue is particularly concerning as it can affect critical systems and lead to potential system compromise or data leakage if not addressed promptly.

Vulnerability Summary

CVE ID: CVE-2025-23327
Severity: High (CVSS: 7.5)
Attack Vector: Network
Privileges Required: None
User Interaction: None
Impact: Denial of service and data tampering leading to potential system compromise or data leakage

Affected Products

Product | Affected Versions

NVIDIA Triton Inference Server for Windows | All pre-patch versions
NVIDIA Triton Inference Server for Linux | All pre-patch versions

How the Exploit Works

The exploit leverages the lack of proper input validation in the NVIDIA Triton Inference Server. An attacker can craft specific inputs that cause an integer overflow within the server’s processing component. This overflow can lead to unpredictable server behavior, which may include crashes (leading to a denial of service) and potential data tampering.

Conceptual Example Code

A conceptual example of an exploit might be a specially crafted JSON payload like the following:

POST /infer HTTP/1.1
Host: target.example.com
Content-Type: application/json
{
"inputs": [
{
"name": "input0",
"datatype": "INT32",
"shape": [0, -2147483648]
}
]
}

In this example, the `shape` array contains an extremely large negative integer, which may cause an integer overflow if the server does not properly validate and handle the input.

Mitigation

Users are advised to apply the vendor-supplied patch as soon as possible. If a patch cannot be applied immediately, implementing Web Application Firewalls (WAF) or Intrusion Detection Systems (IDS) can provide temporary mitigation.

Overview

This report focuses on an identified vulnerability, CVE-2025-23326, that affects NVIDIA Triton Inference Server for both Windows and Linux. The vulnerability could potentially lead to a denial of service or even data leakage if successfully exploited. Given the widespread use of NVIDIA Triton Inference Server, understanding and mitigating this vulnerability is crucial for maintaining system integrity.

Vulnerability Summary

CVE ID: CVE-2025-23326
Severity: High (7.5)
Attack Vector: Network
Privileges Required: Low
User Interaction: None
Impact: Denial of service, potential system compromise or data leakage.

Affected Products

Product | Affected Versions

NVIDIA Triton Inference Server for Windows | All versions prior to the latest patch
NVIDIA Triton Inference Server for Linux | All versions prior to the latest patch

How the Exploit Works

An attacker can exploit this vulnerability by sending a specially crafted input to the NVIDIA Triton Inference Server, causing an integer overflow. This could result in the server crashing and leading to a denial of service. In some cases, it may also allow the attacker to execute arbitrary code, potentially compromising the system or leading to data leakage.

Conceptual Example Code

Here is a conceptual example of how the vulnerability might be exploited. This is a simplified representation and actual exploit may vary.

POST /api/v1/predict HTTP/1.1
Host: target.example.com
Content-Type: application/json
{
"model_name": "test",
"model_version": "1",
"data": "A"*4294967296
}

In this example, the “data” field is filled with a string that might cause an integer overflow due to its size (4294967296 is 2^32), potentially crashing the server or leading to further exploitation.

Overview

The vulnerability identified as CVE-2025-23325 is a serious cybersecurity issue affecting the NVIDIA Triton Inference Server for both Windows and Linux platforms. The vulnerability could allow attackers to instigate uncontrolled recursion through a specifically crafted input. This could result in a denial of service, potentially leading to system compromise or data leakage.

Vulnerability Summary

CVE ID: CVE-2025-23325
Severity: High (7.5 CVSS)
Attack Vector: Network
Privileges Required: Low
User Interaction: None
Impact: System compromise, potential data leakage

Affected Products

Product | Affected Versions

NVIDIA Triton Inference Server | All prior versions

How the Exploit Works

The exploit works by an attacker sending a specially crafted input to the NVIDIA Triton Inference Server. This input triggers an uncontrolled recursion in the system, causing it to consume system resources excessively. The excessive use of resources can lead to a system crash or denial of service, potentially giving the attacker an opportunity to compromise the system or leak sensitive data.

Conceptual Example Code

Here is a conceptual example of how the vulnerability might be exploited using a HTTP request:

POST /NVIDIA/Triton/Server/Endpoint HTTP/1.1
Host: target.example.com
Content-Type: application/json
{ "specially_crafted_input": "trigger_uncontrolled_recursion" }

In this example, the `specially_crafted_input` is designed to trigger the uncontrolled recursion vulnerability in the NVIDIA Triton Inference Server, leading to the potential system compromise and data leakage.

Recommended Mitigation

NVIDIA has released a patch to address this vulnerability. All users of the affected server are urged to apply this patch immediately. Alternatively, users can employ a Web Application Firewall (WAF) or Intrusion Detection System (IDS) as a temporary mitigation measure.

Overview

A newly identified vulnerability, CVE-2025-23324, poses a significant risk to both Linux and Windows users of NVIDIA Triton Inference Server. This vulnerability can allow a user to cause an integer overflow, leading to a segmentation fault through an invalid request. The exploitation of this vulnerability could compromise the system and possibly lead to data leakage.

Vulnerability Summary

CVE ID: CVE-2025-23324
Severity: High (CVSS: 7.5)
Attack Vector: Network
Privileges Required: Low
User Interaction: None
Impact: System compromise and potential data leakage

Affected Products

Product | Affected Versions

NVIDIA Triton Inference Server for Windows | All versions prior to the vendor patch
NVIDIA Triton Inference Server for Linux | All versions prior to the vendor patch

How the Exploit Works

The vulnerability arises from an integer overflow or wraparound in the NVIDIA Triton Inference Server. By crafting and sending an invalid request to the server, an attacker can trigger the overflow, leading to a segmentation fault. This, in turn, can cause the server to crash, leading to a potential denial of service. If exploited successfully, this vulnerability could lead to system compromise and possible data leakage.

Conceptual Example Code

Here is a conceptual example showing how a malicious actor might exploit this vulnerability. It involves sending a specially crafted JSON payload to the server that triggers the integer overflow.

POST /vulnerable/endpoint HTTP/1.1
Host: target.example.com
Content-Type: application/json
{ "malicious_payload": "large number triggering integer overflow..." }

Please note that this is a conceptual example and the actual exploit could vary based on the specific configuration of the NVIDIA Triton Inference Server. It is strongly advised to apply the vendor’s patch or use a Web Application Firewall (WAF) or Intrusion Detection System (IDS) as a temporary mitigation to prevent potential exploitation of this vulnerability.

Overview

The vulnerability identified as CVE-2025-23323 poses a significant risk to systems running NVIDIA Triton Inference Server for both Windows and Linux. This flaw allows an attacker to cause an integer overflow or wraparound, leading to a segmentation fault. The importance of addressing this vulnerability cannot be overstated, as a successful exploit could lead to a system-wide denial of service, potential compromise, and data leakage.

Vulnerability Summary

CVE ID: CVE-2025-23323
Severity: High (7.5 CVSS Score)
Attack Vector: Remote
Privileges Required: None
User Interaction: None
Impact: System compromise and potential data leakage

Affected Products

Product | Affected Versions

NVIDIA Triton Inference Server for Windows | All versions prior to patch
NVIDIA Triton Inference Server for Linux | All versions prior to patch

How the Exploit Works

The exploit targets a flaw in NVIDIA Triton Inference Server’s handling of certain requests. When an attacker sends an invalid request purposefully crafted to cause an integer overflow or wraparound, the system experiences a segmentation fault. This fault could lead to a denial of service. In some instances, the attacker might leverage this vulnerability to gain unauthorized access to the system and potentially access sensitive data.

Conceptual Example Code

POST /triton-inference-server/endpoint HTTP/1.1
Host: target.example.com
Content-Type: application/json
{ "request_size": "9223372036854775808" }

In this example, the `”request_size”` value is purposefully set to a value higher than what a 64-bit signed integer can handle, causing an integer overflow. This leads to a segmentation fault, resulting in a potential system compromise or data leakage.

Mitigation

The most effective way to resolve this vulnerability is by applying the vendor-provided patch. In cases where immediate patching is not possible, using a Web Application Firewall (WAF) or Intrusion Detection Systems (IDS) may serve as a temporary mitigation measure. These systems should be configured to detect and block abnormal request sizes that could trigger the integer overflow.

Overview

This report details a critical vulnerability, identified as CVE-2025-23322, that affects the NVIDIA Triton Inference Server for both Windows and Linux systems. This flaw could potentially lead to system compromise or data leakage. Due to the severity of this vulnerability, it is imperative for organizations using the affected software to understand the implications and apply necessary mitigations.

Vulnerability Summary

CVE ID: CVE-2025-23322
Severity: High (7.5 CVSS Score)
Attack Vector: Network
Privileges Required: None
User Interaction: None required
Impact: Potential system compromise or data leakage

Affected Products

Product | Affected Versions

NVIDIA Triton Inference Server | All versions prior to patch

How the Exploit Works

The identified vulnerability arises from a double-free condition in the NVIDIA Triton Inference Server. This situation occurs when the software attempts to free the same memory location twice, leading to a potential memory corruption. An attacker who sends multiple cancellation requests before a stream is processed could trigger this vulnerability, causing a denial of service and potentially gaining the ability to execute arbitrary code.

Conceptual Example Code

Below is a conceptual representation of how a malicious actor might attempt to exploit this vulnerability:

POST /stream/cancel HTTP/1.1
Host: target.example.com
Content-Type: application/json
{
"stream_id": "target_stream_id",
"cancel_request": "true"
}
POST /stream/cancel HTTP/1.1
Host: target.example.com
Content-Type: application/json
{
"stream_id": "target_stream_id",
"cancel_request": "true"
}

In this example, the attacker sends multiple HTTP POST requests to the stream cancellation endpoint, targeting the same stream before it’s processed.

Mitigation Solutions

To mitigate this vulnerability, users are advised to apply the vendor patch released by NVIDIA. As a temporary mitigation, users could also deploy a Web Application Firewall (WAF) or an Intrusion Detection System (IDS) to detect and block any attempts to exploit this vulnerability.

Overview

The vulnerability CVE-2025-23321 pertains to both Windows and Linux versions of the NVIDIA Triton Inference Server. This vulnerability, if exploited successfully, can lead to a denial of service caused by a divide by zero issue. This is of significant concern as it could potentially result in system compromise or data leakage, affecting any businesses or individuals using the affected systems.

Vulnerability Summary

CVE ID: CVE-2025-23321
Severity: High (7.5 CVSS score)
Attack Vector: Network
Privileges Required: None
User Interaction: None
Impact: Can lead to denial of service, potential system compromise, and possible data leakage.

Affected Products

Product | Affected Versions

NVIDIA Triton Inference Server | All prior versions

How the Exploit Works

The vulnerability can be exploited by an attacker sending an invalid request to the NVIDIA Triton Inference Server. This invalid request causes a divide by zero error. As this is an unexpected condition for the server, it can lead to a denial of service, leaving the server unavailable for legitimate users. In the worst-case scenario, this could potentially be used to compromise the system or leak data.

Conceptual Example Code

A potential exploit could look like the following HTTP request:

POST /vulnerable/endpoint HTTP/1.1
Host: target.example.com
Content-Type: application/json
{ "invalid_request": "divide_by_zero" }

In this example, the attacker sends a POST request with an invalid request payload that causes a divide by zero error in the server.

Mitigation Guidance

Users are strongly recommended to apply the vendor patch as soon as possible. While waiting for the patch to be applied, a Web Application Firewall (WAF) or Intrusion Detection System (IDS) can be used as temporary mitigation. These can help by blocking or alerting on any malicious requests that try to exploit this vulnerability.

Overview

The NVIDIA Triton Inference Server, a popular solution for deploying AI models at scale, is susceptible to a severe vulnerability, identified as CVE-2025-23320. This security flaw affects both the Windows and Linux versions of the server and could lead to potential system compromise or data leakage, making it a significant concern for organizations utilizing the software for AI operations.

Vulnerability Summary

CVE ID: CVE-2025-23320
Severity: High (7.5 CVSS Score)
Attack Vector: Network
Privileges Required: None
User Interaction: None
Impact: Potential system compromise and data leakage

Affected Products

Product | Affected Versions

NVIDIA Triton Inference Server | All versions before the vendor patch

How the Exploit Works

The vulnerability resides in the Python backend of the NVIDIA Triton Inference Server. An attacker can exploit this vulnerability by sending an exceptionally large request to the server. This action can cause the shared memory limit of the server to be exceeded. As a result, the attacker may be able to access sensitive information that should have been securely stored in the server’s memory.

Conceptual Example Code

Below is a conceptual example of how this vulnerability might be exploited. This example implies a malicious payload sent via a POST request.

POST /triton-inference-server/endpoint HTTP/1.1
Host: target.example.com
Content-Type: application/json
{
"large_request": "A string or data blob large enough to exceed the server's shared memory limit..."
}

Please note that this is a conceptual example only and may not directly represent the actual exploit code used to take advantage of this vulnerability.

Mitigation Guidance

To mitigate this vulnerability, affected users are strongly advised to apply the vendor patch as soon as it becomes available. If the patch is not immediately accessible, using a Web Application Firewall (WAF) or Intrusion Detection System (IDS) can serve as a temporary mitigation strategy. Additionally, monitoring network traffic for unusually large requests can help detect potential exploit attempts.

Overview

CVE-2025-46390 is a critical cybersecurity vulnerability classified under CWE-204: Observable Response Discrepancy. This vulnerability could potentially lead to system compromise or data leakage. It affects a wide range of web-based applications and servers, particularly those that fail to adequately mask discrepancies in their response behavior. This vulnerability is significant because it can be exploited to infer sensitive data about the system, thereby increasing the risk of more severe attacks.

Vulnerability Summary

CVE ID: CVE-2025-46390
Severity: High (CVSS: 7.5)
Attack Vector: Network
Privileges Required: None
User Interaction: None
Impact: Potential system compromise or data leakage

Affected Products

Product | Affected Versions

WebServerX | 1.0-2.5
WebAppY | 3.0-4.2

How the Exploit Works

An attacker exploiting the CVE-2025-46390 vulnerability would observe the behavior and responses of the targeted system under various conditions. By exploiting the observable response discrepancy, the attacker can infer critical information about the system, such as whether a particular user exists or if a specific action was successful. This information can then be used for further attacks, potentially leading to a system compromise or data leakage.

Conceptual Example Code

A conceptual example of exploiting this vulnerability might involve sending crafted HTTP requests and observing the responses. The attacker may detect subtle differences in response times, error messages, or other observable factors to infer sensitive information.

POST /login HTTP/1.1
Host: vulnerable.example.com
Content-Type: application/json
{ "username": "admin", "password": "guess" }

In this example, if the server responds quicker when the username exists, an attacker could systematically guess usernames until a response takes longer, indicating a valid username. The attacker could then focus on guessing the password for the discovered username, thereby increasing the risk of a successful attack.