ai-threat-detection

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Kibana 8.19.20, 9.4.5, 9.5.1 Security Update (ESA-2026-126)

Authorization Bypass Through User-Controlled Key in Kibana Leading to Cross-Space Access to Machine Learning Trained Models Description: Authorization Bypass Through User-Controlled Key CWE-639 in Kibana can lead to unauthorized cross-space access via Accessing Functionality Not Properly...

Kibana 8.19.20, and 9.4.5 Security Update (ESA-2026-92)

Missing Authorization in Kibana Machine Learning Leading to Cross-Space Information Disclosure and Unauthorized Data Modification Description: Missing Authorization CWE-862 in Kibana can lead to cross-space information disclosure and unauthorized data modification via Privilege Abuse CAPEC-122...

Kibana 8.19.20 and 9.4.5 Security Update (ESA-2026-88)

Missing Authorization in Kibana Leading to Unauthorized Modification of Machine Learning Trained Model Space Assignments A Kibana Machine Learning capability that removes a saved object from the current space accepts machine learning trained models as a target, but it verifies only the privileges...

CVE-2026-72675: Missing Authorization

Missing Authorization CWE-862 in Kibana can lead to cross-space information disclosure and unauthorized data modification via Privilege Abuse CAPEC-122. Kibana Machine Learning carries out its Elasticsearch operations with elevated internal permissions and relies on a per-request space filter to...

CVE-2026-72642: Use of Out-of-range Pointer Offset

The native inference process that Elasticsearch uses to evaluate uploaded machine learning models accepts a model operation that computes a memory address from an offset supplied inside the model, without validating that the offset stays within the bounds of the underlying storage. A user with th...

PT-2026-71781

Name of the Vulnerable Software and Affected Versions Kibana affected versions not specified Description A Kibana Machine Learning capability that removes a saved object from the current space incorrectly verifies privileges when targeting machine learning trained models. It only checks for...

Characterizing the Variance Envelope: A Multi-Dimensional Analysis of Spectre Telemetry across Architectures and Workloads

Hardware attacks like Spectre exploit built-in processor vulnerabilities, leaving anomalous footprints in Hardware Performance Counter HPC metrics. While machine learning can detect these footprints in controlled settings, static models fail in the real world when confronted with background syste...

PT-2026-71785

Name of the Vulnerable Software and Affected Versions Kibana affected versions not specified Description Missing authorization in Kibana Machine Learning allows for cross-space information disclosure and unauthorized data modification. The system performs Elasticsearch operations using elevated...

CVE-2026-72671: Missing Authorization

A Kibana Machine Learning capability that removes a saved object from the current space accepts machine learning trained models as a target, but it verifies only the privileges that apply to anomaly detection jobs and data frame analytics jobs. A user whose role grants create anomaly detection jo...

Hardware Aware Automated Machine Learning Software Advisory

Summary: A potential security vulnerability for some Hardware Aware Automated Machine Learning software may allow escalation of privilege. Intel is releasing software updates to mitigate this potential vulnerability. Vulnerability Details: CVEID: CVE-2026-34175 Description: Uncontrolled search pa...

Python Now Has a Post-Quantum Encryption Library

This is good: Post-quantum cryptography is now one pip-install away for the entire Python ecosystem. With funding from the Sovereign Tech Agency, we implemented support for ML-KEM, the NIST-standard key-establishment primitive, and ML-DSA, the NIST-standard digital-signature primitive, in...

Diversity Matters: Distributional Feature Coverage Sample Selection for Data-Efficient Backdoor Attacks

Backdoor attacks compromise training data so that a model retains clean accuracy but predicts an attacker-chosen target on triggered inputs. At very low poisoning rates, only a few samples convey the trigger--target association, making poison-sample selection critical. Existing methods typically...

CVE-2026-12261

A flaw was found in the nltk.downloader component of NLTK. This vulnerability allows for cross-package resource and model poisoning. The downloader extracts package archives into shared namespaces and only validates package integrity after extraction. This design flaw enables one package to...

EUVD-2026-54393

A vulnerability in nltk.downloader in nltk/nltk versions = 3.9.4 allows for cross-package resource and model poisoning. The downloader extracts package archives into shared namespaces such as corpora/ and taggers/ instead of package-isolated roots, and validates package integrity only after the...

CVE-2026-12261

A vulnerability in nltk.downloader in nltk/nltk versions = 3.9.4 allows for cross-package resource and model poisoning. The downloader extracts package archives into shared namespaces such as corpora/ and taggers/ instead of package-isolated roots, and validates package integrity only after the...

CVE-2026-12261

A vulnerability in nltk.downloader in nltk/nltk versions = 3.9.4 allows for cross-package resource and model poisoning. The downloader extracts package archives into shared namespaces such as corpora/ and taggers/ instead of package-isolated roots, and validates package integrity only after the...

PT-2026-105507

A vulnerability in nltk.downloader in nltk/nltk versions = 3.9.4 allows for cross-package resource and model poisoning. The downloader extracts package archives into shared namespaces such as corpora/ and taggers/ instead of package-isolated roots, and validates package integrity only after the...

Tool Demo: Topology Analysis with GPML for Detection of Cyberattacks in Water Distribution Networks

Water distribution networks depends on industrial control systems to integrate the physical process with communication network, making them vulnerable to cyberattacks that alter the traffic pattern and network behavior. Traditional detection approaches that rely on raw traffic or protocol...

CVE-2026-47487

NVIDIA Triton Inference Server for Linux contains a vulnerability where a user could cause files outside the model repository to be read, written to, or modified by providing a path in the model name to the Triton MLflow plugin. A successful exploit of this vulnerability might lead to denial of...