Machine learning supports malware detection and threat analysis, but attackers can also exploit biased data, model weaknesses, or poisoned training.
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Machine learning is a way to build software that learns patterns from data and uses them to classify, predict, or make decisions, rather than relying solely on hand-written rules. Models may support malware and phishing detection, user- or entity-behavior analysis, vulnerability prioritization, and automated security triage. Their outputs are probabilistic, so unusual activity can be missed or incorrectly flagged; changing normal behavior can also cause model drift and reduce accuracy.
Security teams must protect both the model and its training data. An attacker may manipulate training examples (data poisoning), craft inputs designed to evade detection (adversarial examples), or extract sensitive information from a model or its data. Controls include trusted data provenance, access restrictions, testing against realistic evasive inputs, monitoring for drift, and human review of high-impact decisions. Where models process personal, proprietary, or security telemetry, collection, retention, and reuse require appropriate privacy and governance controls.
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A flaw in the Google Cloud Vertex AI SDK for Python let an attacker with no access to a victim's project hijack the victim's machine learning model upload and run code inside Google's serving infrastructure
Artificial intelligence (AI) is making its way into security operations quickly, but many practitioners are still struggling to turn early experimentation into consistent operational value. This is because SOCs are adopting AI without an intentional approach to operational integration. Some teams treat it as a shortcut for broken processes. Others attempt to apply machine learning to problems
Talking about AI: Definitions Artificial Intelligence (AI) — AI refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human intelligence, such as decision-making and problem-solving. AI is the broadest concept in this field, encompassing various technologies and methodologies, including Machine Learning (ML) and Deep Learning
Cybersecurity researchers have uncovered two malicious machine learning (ML) models on Hugging Face that leveraged an unusual technique of "broken" pickle files to evade detection
Cybersecurity researchers have disclosed multiple security flaws impacting open-source machine learning (ML) tools and frameworks such as MLflow, H2O, PyTorch, and MLeap that could pave the way for code execution
Cybersecurity researchers have disclosed two security flaws in Google's Vertex machine learning (ML) platform that, if successfully exploited, could allow malicious actors to escalate privileges and exfiltrate models from the cloud
Cybersecurity researchers have uncovered nearly two dozen security flaws spanning 15 different machine learning (ML) related open-source projects
A little over three dozen security vulnerabilities have been disclosed in various open-source artificial intelligence (AI) and machine learning (ML) models, some of which could lead to remote code execution and information theft
Cybersecurity researchers are warning about the security risks in the machine learning (ML) software supply chain following the discovery of more than 20 vulnerabilities that could be exploited to target MLOps platforms
The security risks posed by the Pickle format have once again come to the fore with the discovery of a new "hybrid machine learning (ML) model exploitation technique" dubbed Sleepy Pickle
Artificial Intelligence (AI) company Hugging Face on Friday disclosed that it detected unauthorized access to its Spaces platform earlier this week
As many as 100 malicious artificial intelligence (AI)/machine learning (ML) models have been discovered in the Hugging Face platform
Continuous integration and continuous delivery (CI/CD) misconfigurations discovered in the open-source TensorFlow machine learning framework could have been exploited to orchestrate supply chain attacks
In today's digital landscape, your business data is more than just numbers—it's a powerhouse. Imagine leveraging this data not only for profit but also for enhanced AI and Machine Learning (ML) threat detection
Why Data Exfiltration Detection is Paramount? The world is witnessing an exponential rise in ransomware and data theft employed to extort companies. At the same time, the industry faces numerous critical vulnerabilities in database software and company websites. This evolution paints a dire picture of data exposure and exfiltration that every security leader and team is grappling with. This
Malicious actors are constantly adapting their tactics, techniques, and procedures (TTPs) to adapt to political, technological, and regulatory changes quickly. A few emerging threats that organizations of all sizes should be aware of include the following: Increased use of Artificial Intelligence and Machine Learning: Malicious actors are increasingly leveraging AI and machine learning to
Four different Microsoft Azure services have been found vulnerable to server-side request forgery (SSRF) attacks that could be exploited to gain unauthorized access to cloud resources
The maintainers of the PyTorch package have warned users who have installed the nightly builds of the library between December 25, 2022, and December 30, 2022, to uninstall and download the latest versions following a dependency confusion attack
As many as 350,000 open source projects are believed to be potentially vulnerable to exploitation as a result of a security flaw in a Python module that has remained unpatched for 15 years