Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework

The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased exposure to advanced cyber adversaries and operational vulnerabilities. AI-powered governance and automated decision-making systems are becoming a ke...

Context-Aware Web Attack Detection in Open-Source SIEM Systems Via MITRE ATT&CK-Enriched Behavioral Profiling

Security Information and Event Management SIEM systems aggregate log data from heterogeneous sources to detect coordinated attacks. Traditional rule-based correlation engines struggle to classify multi-step web application attacks because they examine each event without reference to the behaviour...

Explainability Methods for Hardware Trojan Detection: A Systematic Comparison

Hardware trojan detection requires accurate identification and interpretable explanations for security engineers to validate and act on results. This work compares three explainability categories for gate-level trojan detection on the Trust-Hub benchmark: 1 domain-aware property-based analysis of...

EUVD-2021-0430

Malware in sbrugna...

EUVD-2021-0388

Malware in sbrugna...

CVE-2021-41208

TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service via dereferencing nullptrs or via CHECK-failures as well as abuse undefined behavior binding...

SUSE CVE-2021-37652

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for tf.rawops.BoostedTreesCreateEnsemble can result in a use after free error if an attacker supplies specially crafted arguments. The implementation uses a reference counted resource an...

SUSE CVE-2021-37661

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a denial of service in boostedtreescreatequantilestreamresource by using negative arguments. The implementation does not validate that numstreams only contains non-negative numbers. I...

SUSE CVE-2021-37662

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can generate undefined behavior via a reference binding to nullptr in BoostedTreesCalculateBestGainsPerFeature and similar attack can occur in BoostedTreesCalculateBestFeatureSplitV2. The...

SUSE CVE-2021-37664

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to BoostedTreesSparseCalculateBestFeatureSplit. The implementation needs to validate that...

SUSE CVE-2021-41208

TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service via dereferencing nullptrs or via CHECK-failures as well as abuse undefined behavior binding...

GHSA-H6GW-R52C-724R NULL Pointer Dereference and Access of Uninitialized Pointer in TensorFlow

Impact The code for boosted trees in TensorFlow is still missing validation. This allows malicious users to read and write outside of bounds of heap allocated data as well as trigger denial of service via dereferencing nullptrs or via CHECK-failures. This follows after CVE-2021-41208 where these...

NULL Pointer Dereference and Access of Uninitialized Pointer in TensorFlow

Impact The code for boosted trees in TensorFlow is still missing validation. This allows malicious users to read and write outside of bounds of heap allocated data as well as trigger denial of service via dereferencing nullptrs or via CHECK-failures. This follows after CVE-2021-41208 where these...

GHSA-57WX-M983-2F88 Incomplete validation in boosted trees code

Impact The code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service via dereferencing nullptrs or via CHECK-failures as well as abuse undefined behavior binding references to nullptrs. An attacker can also read and write from heap...

Incomplete validation in boosted trees code

Impact The code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service via dereferencing nullptrs or via CHECK-failures as well as abuse undefined behavior binding references to nullptrs. An attacker can also read and write from heap...

PYSEC-2021-400

TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service via dereferencing nullptrs or via CHECK-failures as well as abuse undefined behavior binding...

PYSEC-2021-815

TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service via dereferencing nullptrs or via CHECK-failures as well as abuse undefined behavior binding...

Heap overflow

TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service via dereferencing nullptrs or via CHECK-failures as well as abuse undefined behavior binding...

PYSEC-2021-617

TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service via dereferencing nullptrs or via CHECK-failures as well as abuse undefined behavior binding...

PYSEC-2021-815

TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service via dereferencing nullptrs or via CHECK-failures as well as abuse undefined behavior binding...