80 matches found
zombieguard
ZombieGuard A defensive scanner for structural metadata evasion in ZIP archives. ZombieGuard detects contradictions between ZIP local file headers, central directory records, and payload characteristics. These contradictions can be used to make archive contents appear empty or harmless to one...
CVE-2026-92786
A flaw was found in LightGBM. The software does not properly validate data within text models, which can be exploited by an attacker. By crafting a malicious model file, an attacker can trigger an out-of-bounds write during the SHAP prediction process. This vulnerability could lead to arbitrary...
Unmasking Shortcut Learning in IoT Intrusion Detection: A Forensic, Multi-Paradigm Evaluation of Feature Dependence and Data Leakage
Machine learning-based Network Intrusion Detection Systems often report near-perfect performance on IoT benchmarks. However, whether these models learn generalizable attack behavior or exploit spurious dataset shortcuts- such as static testbed IP/MAC addresses and chronological recording...
Out-of-bounds Write
Overview Affected versions of this package are vulnerable to Out-of-bounds Write in the parsing process of text models due to insufficient validation of child and split array values. An attacker can achieve arbitrary memory writes by supplying a specially crafted model file with invalid node...
CVE-2026-92786
LightGBM through 4.7.0 fails to validate child and split array values when parsing text models, allowing attackers to write out-of-bounds memory during SHAP prediction. Attackers can craft malicious model files with invalid node references that trigger out-of-bounds writes at attacker-chosen...
CVE-2026-92786 LightGBM through 4.7.0 Out-of-Bounds Write via Crafted Model
LightGBM through 4.7.0 fails to validate child and split array values when parsing text models, allowing attackers to write out-of-bounds memory during SHAP prediction. Attackers can craft malicious model files with invalid node references that trigger out-of-bounds writes at attacker-chosen...
EUVD-2026-81049
LightGBM through 4.7.0 fails to validate child and split array values when parsing text models, allowing attackers to write out-of-bounds memory during SHAP prediction. Attackers can craft malicious model files with invalid node references that trigger out-of-bounds writes at attacker-chosen...
CVE-2026-92786 LightGBM through 4.7.0 Out-of-Bounds Write via Crafted Model
LightGBM through 4.7.0 fails to validate child and split array values when parsing text models, allowing attackers to write out-of-bounds memory during SHAP prediction. Attackers can craft malicious model files with invalid node references that trigger out-of-bounds writes at attacker-chosen...
CVE-2026-92786 LightGBM through 4.7.0 Out-of-Bounds Write via Crafted Model
LightGBM through 4.7.0 fails to validate child and split array values when parsing text models, allowing attackers to write out-of-bounds memory during SHAP prediction. Attackers can craft malicious model files with invalid node references that trigger out-of-bounds writes at attacker-chosen...
CVE-2026-92786
LightGBM versions through 4.7.0 are affected by an out-of-bounds write vulnerability triggered during SHAP prediction (feature contribution computation). The root cause is a failure to validate child and split array values when parsing text model files. An attacker can craft a malicious model fil...
CVE-2026-92786: Out-of-bounds Write
LightGBM through 4.7.0 fails to validate child and split array values when parsing text models, allowing attackers to write out-of-bounds memory during SHAP prediction. Attackers can craft malicious model files with invalid node references that trigger out-of-bounds writes at attacker-chosen...
PT-2026-94063
Name of the Vulnerable Software and Affected Versions LightGBM versions prior to 4.7.1 Description Failure to validate child and split array values when parsing text models allows for out-of-bounds memory writes during SHAP SHapley Additive exPlanations, a method used to explain the output of...
Self-Verifying Anomaly Detection Using Explainable AI for Cybersecurity of DER Networks
The rapid growth of Distributed Energy Resources DERs has significantly expanded the cyber attack surface of modern power grids. Furthermore, increasing sophistication in attack techniques demands anomaly detection systems ADS that are accurate, interpretable, and reliable to support DER...
SSHafe: A Real-Time SSH Brute Force Attack Detection and Novel Credential Rotation Standard
SSH remains a critical yet heavily targeted protocol for remote system administration, with password-based authentication exposing servers to large-scale brute-force, dictionary, and credential-spray attacks. Existing rule-based defences such as Fail2Ban fail to detect slow, distributed, or...
CLSA-2026-1776904627 TuxCare security update for lightgbm (1 CVE)
TuxCare rebuilt lightgbm to address 1 CVE. Fixed in 3.3.5.post1+tuxcare...
PT-2026-102750
TuxCare rebuilt lightgbm to address 1 CVE. Fixed in 3.3.5.post1+tuxcare...
A Multi-Objective AutoML-Based Efficient Intrusion Detection System for EV Charging Networks
Electric Vehicle Charging Systems EVCSs are increasingly connected with Internet of Things IoT devices, which improves charging intelligence but also expands their exposure to cyber-attacks. Intrusion Detection Systems IDSs are essential for securing EV charging networks; however, conventional...
MLFlow unsafe deserialization
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.23.0 or newer, enabling a maliciously uploaded LightGBM scikit-learn model to run arbitrary code on an end user’s system when interacted with...
PYSEC-2026-1644 MLFlow unsafe deserialization
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.23.0 or newer, enabling a maliciously uploaded LightGBM scikit-learn model to run arbitrary code on an end user’s system when interacted with...