machine learning | Breaking Cybersecurity News | The Hacker News

AI is changing cybersecurity faster than many defenders realize. Attackers are already using AI to automate reconnaissance, generate sophisticated phishing lures, and exploit vulnerabilities before security teams can react. Meanwhile, defenders are overwhelmed by massive amounts of data and alerts, struggling to process information quickly enough to identify real threats. AI offers a way to level the playing field, but only if security professionals learn to apply it effectively. Organizations are beginning to integrate AI into security workflows, from digital forensics to vulnerability assessments and endpoint detection. AI allows security teams to ingest and analyze more data than ever before, transforming traditional security tools into powerful intelligence engines. AI has already demonstrated its ability to accelerate investigations and uncover unknown attack paths, but many companies are hesitant to fully embrace it. Many AI models are implemented with such velocity that they r...

AI agents have rapidly evolved from experimental technology to essential business tools. The OWASP framework explicitly recognizes that Non-Human Identities play a key role in agentic AI security. Their analysis highlights how these autonomous software entities can make decisions, chain complex actions together, and operate continuously without human intervention. They're no longer just tools, but an integral and significant part of your organization's workforce. Consider this reality: Today's AI agents can analyze customer data, generate reports, manage system resources, and even deploy code, all without a human clicking a single button. This shift represents both tremendous opportunity and unprecedented risk. AI Agents are only as secure as their NHIs Here's what security leaders are not necessarily considering: AI agents don't operate in isolation . To function, they need access to data, systems, and resources. This highly privileged, often overlooked acces...

A dataset used to train large language models (LLMs) has been found to contain nearly 12,000 live secrets, which allow for successful authentication. The findings once again highlight how hard-coded credentials pose a severe security risk to users and organizations alike, not to mention compounding the problem when LLMs end up suggesting insecure coding practices to their users. Truffle Security said it downloaded a December 2024 archive from Common Crawl , which maintains a free, open repository of web crawl data. The massive dataset contains over 250 billion pages spanning 18 years.  The archive specifically contains 400TB of compressed web data, 90,000 WARC files (Web ARChive format), and data from 47.5 million hosts across 38.3 million registered domains. The company's analysis found that there are 219 different secret types in the Common Crawl archive, including Amazon Web Services (AWS) root keys, Slack webhooks, and Mailchimp API keys. "'Live' secrets ar...

Security Service Edge (SSE) platforms have become the go-to architecture for securing hybrid work and SaaS access. They promise centralized enforcement, simplified connectivity, and consistent policy control across users and devices. But there's a problem: they stop short of where the most sensitive user activity actually happens—the browser. This isn't a small omission. It's a structural limitation. And it's leaving organizations exposed in the one place they can't afford to be: the last mile of user interaction. A new report Reevaluating SSEs: A Technical Gap Analysis of Last-Mile Protection analyzing gaps in SSE implementations reveals where current architectures fall short—and why many organizations are reevaluating how they protect user interactions inside the browser. The findings point to a fundamental visibility challenge at the point of user action. SSEs deliver value for what they're designed to do—enforce network-level policies and route traffic securely between en...

Organizations today face relentless cyber attacks, with high-profile breaches hitting the headlines almost daily. Reflecting on a long journey in the security field, it's clear this isn't just a human problem—it's a math problem. There are simply too many threats and security tasks for any SOC to manually handle in a reasonable timeframe. Yet, there is a solution. Many refer to it as SOC 3.0—an AI-augmented environment that finally lets analysts do more with less and shifts security operations from a reactive posture to a proactive force. The transformative power of SOC 3.0 will be detailed later in this article, showcasing how artificial intelligence can dramatically reduce workload and risk, delivering world-class security operations that every CISO dreams of. However, to appreciate this leap forward, it's important to understand how the SOC evolved over time and why the steps leading up to 3.0 set the stage for a new era of security operations. A brief history of the SOC For deca...

AI is everywhere now, transforming how businesses operate and how users engage with apps, devices, and services. A lot of applications now have some Artificial Intelligence inside, whether supporting a chat interface, intelligently analyzing data or matching user preferences. No question AI benefits users, but it also brings new security challenges, especially Identity-related security challenges. Let's explore what these challenges are and what you can do to face them with Okta. Which AI? Everyone talks about AI, but this term is very general, and several technologies fall under this umbrella. For example, symbolic AI uses technologies such as logic programming, expert systems, and semantic networks. Other approaches use neural networks, Bayesian networks, and other tools. Newer Generative AI uses Machine Learning (ML) and Large Language Models (LLM) as core technologies to generate content such as text, images, video, audio, etc. Many of the applications we use most often toda...

Google has stepped in to clarify that a newly introduced Android System SafetyCore app does not perform any client-side scanning of content. "Android provides many on-device protections that safeguard users against threats like malware, messaging spam and abuse protections, and phone scam protections, while preserving user privacy and keeping users in control of their data," a spokesperson for the company told The Hacker News when reached for comment. "SafetyCore is a new Google system service for Android 9+ devices that provides the on-device infrastructure for securely and privately performing classification to help users detect unwanted content. Users are in control over SafetyCore and SafetyCore only classifies specific content when an app requests it through an optionally enabled feature." SafetyCore (package name "com.google.android.safetycore") was first introduced by Google in October 2024, as part of a set of security measures designed to...

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. "The pickle files extracted from the mentioned PyTorch archives revealed the malicious Python content at the beginning of the file," ReversingLabs researcher Karlo Zanki said in a report shared with The Hacker News. "In both cases, the malicious payload was a typical platform-aware reverse shell that connects to a hard-coded IP address." The approach has been dubbed nullifAI, as it involves clearcut attempts to sidestep existing safeguards put in place to identify malicious models. The Hugging Face repositories have been listed below - glockr1/ballr7 who-r-u0000/0000000000000000000000000000000000000 It's believed that the models are more of a proof-of-concept (PoC) than an active supply chain attack scenario. The pickle serialization format, used commonly for dis...

Italy's data protection watchdog has blocked Chinese artificial intelligence (AI) firm DeepSeek's service within the country, citing a lack of information on its use of users' personal data. The development comes days after the authority, the Garante, sent a series of questions to DeepSeek, asking about its data handling practices and where it obtained its training data. In particular, it wanted to know what personal data is collected by its web platform and mobile app, from which sources, for what purposes, on what legal basis, and whether it is stored in China. In a statement issued January 30, 2025, the Garante said it arrived at the decision after DeepSeek provided information that it said was "completely insufficient." The entities behind the service, Hangzhou DeepSeek Artificial Intelligence and Beijing DeepSeek Artificial Intelligence, have "declared that they do not operate in Italy and that European legislation does not apply to them," it...

Cybersecurity researchers have shed light on a new jailbreak technique that could be used to get past a large language model's (LLM) safety guardrails and produce potentially harmful or malicious responses. The multi-turn (aka many-shot) attack strategy has been codenamed Bad Likert Judge by Palo Alto Networks Unit 42 researchers Yongzhe Huang, Yang Ji, Wenjun Hu, Jay Chen, Akshata Rao, and Danny Tsechansky. "The technique asks the target LLM to act as a judge scoring the harmfulness of a given response using the Likert scale , a rating scale measuring a respondent's agreement or disagreement with a statement," the Unit 42 team said . "It then asks the LLM to generate responses that contain examples that align with the scales. The example that has the highest Likert scale can potentially contain the harmful content." The explosion in popularity of artificial intelligence in recent years has also led to a new class of security exploits called prompt in...

Cybersecurity researchers have found that it's possible to use large language models (LLMs) to generate new variants of malicious JavaScript code at scale in a manner that can better evade detection. "Although LLMs struggle to create malware from scratch, criminals can easily use them to rewrite or obfuscate existing malware, making it harder to detect," Palo Alto Networks Unit 42 researchers said in a new analysis. "Criminals can prompt LLMs to perform transformations that are much more natural-looking, which makes detecting this malware more challenging." With enough transformations over time, the approach could have the advantage of degrading the performance of malware classification systems, tricking them into believing that a piece of nefarious code is actually benign. While LLM providers have increasingly enforced security guardrails to prevent them from going off the rails and producing unintended output, bad actors have advertised tools like WormGPT...

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. The vulnerabilities, discovered by JFrog, are part of a broader collection of 22 security shortcomings the supply chain security company first disclosed last month. Unlike the first set that involved flaws on the server-side, the newly detailed ones allow exploitation of ML clients and reside in libraries that handle safe model formats like Safetensors . "Hijacking an ML client in an organization can allow the attackers to perform extensive lateral movement within the organization," the company said . "An ML client is very likely to have access to important ML services such as ML Model Registries or MLOps Pipelines." This, in turn, could expose sensitive information such as model registry credentials, effectively permitting a malicious actor to back...

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. "By exploiting custom job permissions, we were able to escalate our privileges and gain unauthorized access to all data services in the project," Palo Alto Networks Unit 42 researchers Ofir Balassiano and Ofir Shaty said in an analysis published earlier this week. "Deploying a poisoned model in Vertex AI led to the exfiltration of all other fine-tuned models, posing a serious proprietary and sensitive data exfiltration attack risk." Vertex AI is Google's ML platform for training and deploying custom ML models and artificial intelligence (AI) applications at scale. It was first introduced in May 2021. Crucial to leveraging the privilege escalation flaw is a feature called Vertex AI Pipelines , which allows users to automat...

In recent years, artificial intelligence (AI) has begun revolutionizing Identity Access Management (IAM), reshaping how cybersecurity is approached in this crucial field. Leveraging AI in IAM is about tapping into its analytical capabilities to monitor access patterns and identify anomalies that could signal a potential security breach. The focus has expanded beyond merely managing human identities — now, autonomous systems, APIs, and connected devices also fall within the realm of AI-driven IAM, creating a dynamic security ecosystem that adapts and evolves in response to sophisticated cyber threats. The Role of AI and Machine Learning in IAM AI and machine learning (ML) are creating a more robust, proactive IAM system that continuously learns from the environment to enhance security. Let's explore how AI impacts key IAM components: Intelligent Monitoring and Anomaly Detection AI enables continuous monitoring of both human and non-human identities , including APIs, service acc...