Author: Ameeba

Overview

An issue has been identified in GitLab CE/EE, affecting multiple versions of the software, that allows unauthenticated users to initiate a Denial of Service (DoS) attack through the upload of large, specifically crafted JSON files. This vulnerability has significant implications as it can potentially compromise the system or lead to data leakage.

Vulnerability Summary

CVE ID: CVE-2025-10858
Severity: High (7.5 CVSS Score)
Attack Vector: Network
Privileges Required: None
User Interaction: None
Impact: Denial of Service and potential system compromise or data leakage

Affected Products

Product | Affected Versions

GitLab CE | All versions before 18.2.7
GitLab EE | 18.3 before 18.3.3, 18.4 before 18.4.1

How the Exploit Works

The vulnerability allows unauthenticated users to execute a Denial of Service attack by uploading large, specifically crafted JSON files. This exploit causes excessive resource consumption on the server, effectively causing a DoS condition. In some cases, this could potentially lead to a system compromise or data leakage.

Conceptual Example Code

Below is a conceptual example of how an attacker might exploit this vulnerability:

POST /upload_endpoint HTTP/1.1
Host: target.example.com
Content-Type: application/json
{ "large_malicious_json": "..." }

In this example, the attacker sends a POST request to the `/upload_endpoint` of the target server, containing a large malicious JSON payload, causing the server to consume excessive resources and trigger a DoS condition.

Overview

This report details a recently discovered vulnerability, identified as CVE-2025-59404, affecting the Flock Safety Bravo Edge AI Compute Device. This vulnerability is a significant concern due to the device’s bootloader being unlocked, allowing direct modification of partitions and bypass of Android Verified Boot (AVB). The impact of this vulnerability is potentially severe, including system compromise or data leakage, and it should be immediately addressed to ensure the security of the affected systems.

Vulnerability Summary

CVE ID: CVE-2025-59404
Severity: High (7.5 CVSS Score)
Attack Vector: Local
Privileges Required: None
User Interaction: None
Impact: Potential system compromise or data leakage

Affected Products

Product | Affected Versions

Flock Safety Bravo Edge AI Compute Device | BRAVO_00.00_local_20241017

How the Exploit Works

The Flock Safety Bravo Edge AI Compute Device ships with an unlocked bootloader. This means that an attacker can bypass Android Verified Boot (AVB) and directly modify partitions. This can lead to unauthorized system access, alteration of system behavior, or the installation of malicious software. This vulnerability creates a significant risk of system compromise and data leakage.

Conceptual Example Code

While the specifics of an exploit will vary based on the attacker’s objectives, a conceptualized shell command exploiting this vulnerability might look like this:

fastboot flash system malicious_system.img

In this example, `malicious_system.img` is an Android system image that the attacker has modified for malicious purposes. Using the fastboot utility, which is accessible due to the unlocked bootloader, the attacker replaces the device’s system partition with their malicious system image. This could provide them with system-level access and control.

Mitigation Guidance

To mitigate this vulnerability, users should apply the vendor’s patch as soon as it is available. As a temporary measure, the use of a Web Application Firewall (WAF) or Intrusion Detection System (IDS) can help detect and prevent exploit attempts. However, these measures cannot fully protect against the vulnerability, and patching the device should be prioritized.

Overview

This report details the buffer overflow vulnerability in libsmb2 6.2+ identified as CVE-2025-57632. The vulnerability impacts systems running this particular software version, presenting a significant threat as it can potentially allow an attacker to execute arbitrary code, leading to system compromise or data leakage.

Vulnerability Summary

CVE ID: CVE-2025-57632
Severity: High (7.5 CVSS)
Attack Vector: Network
Privileges Required: None
User Interaction: None
Impact: System compromise, data leakage, memory corruption, and crashes

Affected Products

Product | Affected Versions

libsmb2 | 6.2+

How the Exploit Works

The vulnerability stems from improper handling and validation of SMB2 chained PDUs inside libsmb2. When processing these chained PDUs, the software repeatedly calls the function smb2_add_iovector() to append to a fixed-size iovec array without checking the upper bound, v->niov, which is capped at 256 (SMB2_MAX_VECTORS).
An attacker can exploit this vulnerability by crafting responses with numerous chained PDUs, causing an overflow of v->niov. This results in heap out-of-bounds writes, which lead to memory corruption, system crashes, and potentially arbitrary code execution. Furthermore, the SMB2_OPLOCK_BREAK path bypasses message ID validation, making it easier for an attacker to exploit this vulnerability.

Conceptual Example Code

Below is a conceptual example of how an attacker might exploit this vulnerability. This pseudocode represents a malicious SMB2 response with an abnormal number of chained PDUs:

SMB2_Header {
ProtocolId: SMB2,
MessageId: 0x1,
...
}
SMB2_Chained_PDU {
NextCommand: 0x1,
...
}
...
SMB2_Chained_PDU {
NextCommand: 0x100, // Exceeds SMB2_MAX_VECTORS
...
}

This code results in an overflow of the v->niov variable, leading to memory corruption and potential arbitrary code execution. By sending a large number of these responses, an attacker can crash the system or even take control of it.

Overview

The cybersecurity vulnerability identified as CVE-2025-48707 is a critical threat to Stormshield Network Security (SNS) systems running on versions prior to 5.0.1. The vulnerability pertains to the TPM authentication process, which, under specific HA use cases, results in shared secret among administrators. This flaw could potentially lead to unauthorized system access and subsequent data leakage, thus posing a significant risk to the confidentiality and integrity of the systems in question.

Vulnerability Summary

CVE ID: CVE-2025-48707
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

Stormshield Network Security | Before 5.0.1

How the Exploit Works

The exploit works by taking advantage of the TPM authentication process in certain HA use cases. Normally, the TPM authentication information is unique to each administrator. However, due to this vulnerability, the same secret can be shared among multiple administrators. An attacker can leverage this flaw to gain unauthorized access to the system, impersonate an administrator, and potentially compromise the system or leak sensitive data.

Conceptual Example Code

The following is a conceptual example of how the vulnerability might be exploited. Please note that this is a hypothetical scenario meant to illustrate the nature of the vulnerability and is not an actual exploit code.

# Attacker gains network access
attacker@host:~$ ssh user@target.example.com
# Attacker uses shared secret to bypass TPM authentication
attacker@host:~$ sudo sns_tpm_auth --use-shared-secret
# Attacker now has unauthorized administrative access
attacker@host:~$ sudo whoami
root

Overview

We are addressing the CVE-2025-10880 vulnerability that impacts all versions of Dingtian DT-R002. This vulnerability allows unauthenticated GET requests to extract proprietary protocols passwords, posing a significant threat to system security and data integrity. Immediate action is required to prevent potential system compromise or data leakage.

Vulnerability Summary

CVE ID: CVE-2025-10880
Severity: High (7.5 CVSS Score)
Attack Vector: Network
Privileges Required: None
User Interaction: None
Impact: System compromise and potential data leakage

Affected Products

Product | Affected Versions

Dingtian DT-R002 | All versions

How the Exploit Works

The exploit takes advantage of a vulnerability in Dingtian DT-R002’s Insufficiently Protected Credentials. An attacker can remotely extract the proprietary “Dingtian Binary” protocol passwords by sending an unauthenticated GET request. This vulnerability does not require user interaction or any higher privileges, making it a severe security threat.

Conceptual Example Code

Below is a conceptual example of how the vulnerability might be exploited. This is a simplified representation of a malicious HTTP GET request:

GET /proprietary/endpoint HTTP/1.1
Host: target.example.com

Upon sending this request, the attacker would receive a response containing the proprietary “Dingtian Binary” protocol password, granting them unauthorized access to the system.

Mitigation Guidance

To mitigate this vulnerability, users should immediately apply the patch provided by the vendor. If a patch is not available, use a Web Application Firewall (WAF) or an Intrusion Detection System (IDS) as a temporary mitigation. Regularly updating and patching systems also forms a crucial part of maintaining security against such vulnerabilities.

Overview

The vulnerability CVE-2025-57446 is a critical security flaw found in the O-RAN Near Realtime RIC ric-plt-submgr in the J-Release environment. This vulnerability allows remote attackers to cause a denial of service (DoS) via a specially crafted request to the Subscription Manager API component. The flaw has significant implications for system availability, potentially leading to system compromise or data leakage.

Vulnerability Summary

CVE ID: CVE-2025-57446
Severity: High (7.5 CVSS score)
Attack Vector: Network
Privileges Required: None
User Interaction: None
Impact: Denial of Service, potential system compromise, and data leakage

Affected Products

Product | Affected Versions

O-RAN Near Realtime RIC ric-plt-submgr | J-Release

How the Exploit Works

The exploit works by sending a specially crafted request to the Subscription Manager API component of the O-RAN Near Realtime RIC ric-plt-submgr. The malformed request triggers an error in the system, causing an unexpected condition that leads to a denial of service. Furthermore, in some circumstances, this could lead to a system compromise or data leakage.

Conceptual Example Code

Below is a conceptual example of how the vulnerability might be exploited. This example represents a malicious HTTP request to the vulnerable API endpoint.

POST /api/subscription HTTP/1.1
Host: target.example.com
Content-Type: application/json
{ "malicious_payload": "crafted_request_that_causes_dos" }

Mitigation Measures

Until a patch is provided by the vendor to rectify this vulnerability, it is recommended to use Web Application Firewalls (WAF) or Intrusion Detection Systems (IDS) as a temporary mitigation. These measures can help detect and block malicious requests, thereby limiting the potential impact of this vulnerability.

Overview

A significant issue has been identified in pyTorch v2.7.0, a popular open-source machine learning library. This vulnerability, identified as CVE-2025-55560, can lead to a Denial of Service (DoS) attack, potentially compromising systems and leading to data leakage. Developers, system administrators, and organizations using affected versions are advised to implement the necessary patches or mitigation strategies to prevent a potential exploit.

Vulnerability Summary

CVE ID: CVE-2025-55560
Severity: High (7.5 CVSS Score)
Attack Vector: Network
Privileges Required: None
User Interaction: None
Impact: System compromise or data leakage

Affected Products

Product | Affected Versions

PyTorch | v2.7.0

How the Exploit Works

The exploit takes advantage of a specific issue in pyTorch v2.7.0, where the combination of torch.Tensor.to_sparse() and torch.Tensor.to_dense() in a PyTorch model can lead to a Denial of Service (DoS) when compiled by Inductor. Attackers can craft malicious models that, when processed, exhaust system resources, causing a DoS condition and potentially leading to system compromise or data leakage.

Conceptual Example Code

The following is a simplified conceptual example of how an attacker might exploit this vulnerability:

import torch
# Define a PyTorch model with the vulnerability
class VulnerableModel(torch.nn.Module):
def forward(self, x):
x = x.to_sparse()
return x.to_dense()
# Compile the model with Inductor
model = VulnerableModel()
# Craft a malicious input that triggers the vulnerability
malicious_input = torch.randn(1000000, 1000000)
# Pass the malicious input to the model
model(malicious_input)

In this example, the malicious_input tensor is large enough to exhaust system resources when the `to_dense()` method is called, causing a DoS condition.

Overview

This report focuses on CVE-2025-55559, a high-severity vulnerability discovered in TensorFlow v2.18.0. This vulnerability, if exploited, can lead to a Denial of Service (DoS) attack, potentially compromising systems or leading to data leakage. It affects all systems utilizing TensorFlow v2.18.0, highlighting the urgent need for mitigation and patching.

Vulnerability Summary

CVE ID: CVE-2025-55559
Severity: High (7.5 CVSS Score)
Attack Vector: Network
Privileges Required: None
User Interaction: None
Impact: Potential system compromise or data leakage

Affected Products

Product | Affected Versions

TensorFlow | v2.18.0

How the Exploit Works

The vulnerability is triggered when padding is set to ‘valid’ in tf.keras.layers.Conv2D within TensorFlow v2.18.0. This incorrect configuration can lead to a buffer overflow condition, causing the system to become unresponsive, leading to a Denial of Service (DoS) situation. Attackers can exploit this vulnerability remotely over a network connection, without requiring any user interaction.

Conceptual Example Code

The following pseudocode outlines a potential exploitation scenario:

import tensorflow as tf
# Create a maliciously configured Conv2D layer
layer = tf.keras.layers.Conv2D(64, (3, 3), padding='valid')
# Prepare a large input tensor
input = tf.random.uniform((1, 3000, 3000, 3))
# Apply the malicious layer
output = layer(input)

In this example, the attacker creates a Conv2D layer with ‘valid’ padding and applies this to a large input tensor. This can cause the system to overflow, leading to the Denial of Service (DoS) condition.

Overview

A critical vulnerability, CVE-2025-55558, has been identified in pytorch v2.7.0, which affects machine learning platforms that employ this version of the software. This vulnerability is of significant concern as it can lead to a buffer overflow, causing a Denial of Service (DoS) and potentially compromising system security or causing data leakage.

Vulnerability Summary

CVE ID: CVE-2025-55558
Severity: High (CVSS: 7.5)
Attack Vector: Network
Privileges Required: Low
User Interaction: None
Impact: Denial of Service, potential system compromise and data leakage

Affected Products

Product | Affected Versions

pytorch | v2.7.0

How the Exploit Works

The vulnerability arises when a PyTorch model, consisting of torch.nn.Conv2d, torch.nn.functional.hardshrink, and torch.Tensor.view-torch.mv(), is compiled by Inductor. The process results in a buffer overflow if the model’s input is not correctly validated. This buffer overflow could then be exploited by an attacker to cause a denial of service, possibly compromising the system or leaking data.

Conceptual Example Code

Below is a conceptual example of how the vulnerability might be exploited. This pseudocode depicts a scenario where a malicious payload triggers the buffer overflow:

# Malicious payload
payload = "A" * 10000  # Oversized input
# PyTorch model
model = torch.nn.Sequential(
torch.nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1),
torch.nn.functional.hardshrink(),
torch.Tensor.view(-1).mv(payload)  # Trigger buffer overflow
)
# Compile with Inductor
inductor.compile(model)

This code would trigger a buffer overflow in the system running this version of pytorch, leading to a Denial of Service (DoS).

Mitigation

Users are advised to apply the vendor-provided patch as soon as possible to correct this vulnerability. As a temporary mitigation strategy, users can deploy a Web Application Firewall (WAF) or an Intrusion Detection System (IDS) to help identify and block exploit attempts.

Overview

The vulnerability CVE-2025-55557 is a critical flaw in the pytorch v2.7.0 application, which can result in Denial of Service (DoS) attacks. This exploitation occurs when a PyTorch model consists of torch.cummin and is compiled by Inductor. The vulnerability affects all systems running pytorch v2.7.0. It’s a pressing matter because successful exploitation may lead to system compromise and potential data leakage.

Vulnerability Summary

CVE ID: CVE-2025-55557
Severity: High (7.5 CVSS)
Attack Vector: Network
Privileges Required: None
User Interaction: None
Impact: Denial of Service, potential system compromise, and data leakage

Affected Products

Product | Affected Versions

pytorch | v2.7.0

How the Exploit Works

The exploit takes advantage of a Name Error in pytorch v2.7.0. When a PyTorch model that includes torch.cummin is compiled by Inductor, an error is triggered. This error can be exploited to cause a Denial of Service. In some cases, this DoS condition may be leveraged by attackers to compromise the system or leak sensitive data.

Conceptual Example Code

Here is a pseudocode representation of how the vulnerability might be exploited:

# Create a PyTorch model with torch.cummin
model = PyTorchModel()
model.add(torch.cummin)
# Compile the model with Inductor
compiled_model = InductorCompiler.compile(model)
# The above operation triggers a Name Error, leading to DoS

Note: The above code is a conceptual representation. The actual exploit might involve the delivery of malicious payloads over the network, potentially through an API endpoint that uses the vulnerable PyTorch model.

Mitigation

To mitigate this vulnerability, apply the vendor-supplied patch immediately. If the patch cannot be applied right away, consider using a Web Application Firewall (WAF) or Intrusion Detection System (IDS) as a temporary measure to prevent exploit attempts.