CVE-2026-44285

FastGPT is an AI Agent building platform. Prior to 4.15.0-beta1, a Server-Side Request Forgery SSRF vulnerability allows an authenticated attacker to bypass the global isInternalAddress network protection and make arbitrary HTTP GET requests to internal network services. This is achieved by...

CVE-2026-44285

FastGPT is affected by an SSRF flaw in the dataset preview API. Before 4.15.0-beta1, an authenticated attacker could bypass isInternalAddress protection and reach internal services by abusing /api/core/dataset/file/getPreviewChunks with the externalFile data import type. The issue is resolved in ...

CVE-2026-44285

FastGPT is an AI Agent building platform. Prior to 4.15.0-beta1, a Server-Side Request Forgery SSRF vulnerability allows an authenticated attacker to bypass the global isInternalAddress network protection and make arbitrary HTTP GET requests to internal network services. This is achieved by...

CVE-2026-44285 FastGPT: SSRF Protection Bypass via `externalFile` in Dataset Preview API

FastGPT is an AI Agent building platform. Prior to 4.15.0-beta1, a Server-Side Request Forgery SSRF vulnerability allows an authenticated attacker to bypass the global isInternalAddress network protection and make arbitrary HTTP GET requests to internal network services. This is achieved by...

CVE-2026-44285 FastGPT: SSRF Protection Bypass via `externalFile` in Dataset Preview API

FastGPT is an AI Agent building platform. Prior to 4.15.0-beta1, a Server-Side Request Forgery SSRF vulnerability allows an authenticated attacker to bypass the global isInternalAddress network protection and make arbitrary HTTP GET requests to internal network services. This is achieved by...

EUVD-2026-33430

FastGPT is an AI Agent building platform. Prior to 4.15.0-beta1, a Server-Side Request Forgery SSRF vulnerability allows an authenticated attacker to bypass the global isInternalAddress network protection and make arbitrary HTTP GET requests to internal network services. This is achieved by...

Improving IoT Intrusion Detection through SMOTE-Based Oversampling and Extended Multi-Model Evaluation on Side-Channel Power Data

The detection of intrusions in IoT-based networks poses challenges that cannot be overcome using traditional machine learning methods. Perhaps the biggest of them is related to the presence of a class imbalance in the side-channel dataset, where the number of samples in the normal class compared ...

Separating Secrets from Placeholders: A Hybrid CNN-CodeBERT Framework for Three-Class Credential Leakage Detection

Credential leakage in public source code repositories poses a critical security threat, with over 23.8 million secrets exposed in 2024 alone. Existing detection tools suffer from high false-positive rates because rigid pattern matching and binary classification schemes fail to distinguish genuine...

FastGPT 代码问题漏洞

FastGPT is an open-source knowledge base question-answering system based on large language models developed by Labring. Versions of FastGPT prior to 4.15.0-beta1 contained code vulnerabilities. These vulnerabilities were caused by server-side request forgery, allowing authenticated attackers to...

PT-2026-44979

Name of the Vulnerable Software and Affected Versions FastGPT versions prior to 4.15.0-beta1 Description An authenticated attacker can bypass the global isInternalAddress network protection to make arbitrary HTTP GET requests to internal network services. This occurs due to an incomplete fix in t...

Towards Demystifying and Repairing LLM-In-The-Loop Vulnerabilities

Large Language ModelsLLMs have been actively integrated into modern software systems as critical components. LLM-in-the-loop vulnerabilities, where vulnerabilities are introduced by LLMs and their dependent downstream components, such as frameworks, introduce new risks. Although some benchmark...

Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation

We present a dataset of adversarial malware samples derived from the public RawMal-TF collection of real-world malware binaries. Using a suite of adversarial malware generators, we construct two sets of adversarial PE files: 44,347 family-labelled samples and 33,596 type-labelled samples, achievi...

FALCON-C: Flow-Based Analysis and Labeling for Connected Vehicular Network Cybersecurity

Along with the recent rise in popularity of Electric Vehicles EVs, Electric Vehicle Supply Equipment EVSE has emerged as a new target for cyber attacks. Therefore, ensuring the security and integrity of network communication between EVSE components and vehicular clients is a significant challenge...

Botnet Detection on CTU-13 Using Lightweight Machine Learning Models

Botnets are among the most persistent cyber threats, enabling large-scale attacks such as spam, credential theft, and distributed denial-of-service DDoS. While deep learning approaches have recently been applied to botnet detection, they are computationally intensive and often lack...

Three Heads Are Better Than One: A Multi-Perspective Reasoning Framework for Enhanced Vulnerability Detection

Automated vulnerability detection is crucial for enhancing software security by identifying potential flaws that attackers could exploit, thereby reducing the reliance on labor-intensive manual code audits. Recent advancements have shifted towards leveraging large language models LLMs for...

Explainable Machine Learning for Phishing Detection on Heterogeneous Datasets with MCP-Enabled Deployment

With the growth in digital transformation and Internet usage, the Social Engineering techniques such as Phishing have become a major concern for the users and the organizations. Phishing attacks involve deceptive techniques to trick users into revealing confidential information that causes...

Filter-Then-Verify: A Multiphase GNN and ModernBERT Framework for Social Engineering Detection in Email Networks

Social engineering attacks exploit human trust rather than software vulnerabilities, making them difficult to detect using conventional filters. We propose a two-stage filter-then-verify framework combining inductive Graph Neural Networks GNNs for structural anomaly detection with a co-attention...

CVE-2026-31237

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization CWE-502 through its predict method. When a user provides a dataset file path to the predict method, the framework automatically determines the file format. If the file is a pickle .pkl file, it is loaded using...

On-Device Interpretable Tsetlin Machine-Based Intrusion Detection for Secure IoMT

The rapid evolution of digital health technologies is redefining healthcare services worldwide. The integration of wireless communication and Internet-enabled medical devices within Internet of Medical Things IoMT networks enables continuous, real-time patient monitoring. However, this increased...

Improperly Controlled Modification of Dynamically-Determined Object Attributes

Overview flowise is a Flowiseai Server Affected versions of this package are vulnerable to Improperly Controlled Modification of Dynamically-Determined Object Attributes through improper handling of the Object.assign process in the dataset service. An attacker can gain unauthorized access to...