Our latest enhancements in SonarQube establish a non-negotiable code verification layer designed to bridge this trust gap, unifying the analysis of first-party, AI-generated, and third-party code.
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Behind a basic age check, researchers say Persona’s system runs extensive identity, watchlist, and adverse-media screening.
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Read on for an exhaustive comparison of the technical architectures of Claude Opus 4.5 and 4.6, an evaluation of their performance across industry-standard benchmarks, and an outline of Sonar’s focus on embracing agentic development.
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The global vulnerability landscape continues to expand rapidly, with thousands of new CVEs published every year. Thus, allowing hackers to weaponize newly disclosed flaws at an instant. Public reporting and threat intelligence analyses consistently show that exploitation often begins within days, and sometimes hours, of disclosure. That reality has fundamentally changed what “vulnerability assessment tools” […]
The post Real-Time Risk Detection with Automated Vulnerability Assessment Tools appeared first on Kratikal Blogs.
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AI agents have moved from novelty to operational reality, acting autonomously across business systems in ways traditional AI security posture management (AISPM) and IAM can’t fully govern. Learn why risk now emerges at runtime, where existing posture tools fall short, and how Agentic SPM enables continuous discovery, runtime decision control, and auditability for autonomous agents.
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Explore lattice-based zero trust identity verification for AI agents. Secure MCP deployments with quantum-resistant encryption and 4D access control.
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Last month, Microsoft quietly confirmed something that should keep every CISO up at night.
As first reported by BleepingComputer and later detailed by TechCrunch, a bug in Microsoft Office allowed Copilot, the AI assistant embedded in millions of enterprise environments, to summarize confidential emails and hand them to users who had no business seeing them. Sensitivity labels? Ignored. Data loss prevention (DLP) policies? Bypassed entirely.
This wasn't the work of a hacker or malware. This was a trusted internal tool doing exactly what it was designed to do: processing data. The AI didn't break in. It was already inside.
The Illusion of the Internal Safe ZoneFor years, security teams have operated under a comforting assumption: internal APIs are safe because they sit behind the gateway. We challenged this myth in our latest Field Guide, but the Microsoft incident proves the reality is far more volatile.
When you deploy an AI agent, you are handing a highly privileged entity the keys to your internal data. You are trusting it to respect every access policy, sensitivity label, and permission boundary you have built. When it doesn't: when it incorrectly processes a context or misreads a label, there is no alarm. No blocked requests at the edge. Just sensitive data, silently served to the wrong person.
The "Confused Deputy" Is Already on Your PayrollSecurity researchers call this the confused deputy problem. It occurs when a trusted entity with legitimate access is tricked (or simply misconfigured) into acting against your interests.
With the rise of the Model Context Protocol (MCP), this problem is about to get dramatically worse. MCP is the "USB-C for AI," designed to let agents plug into any internal data source with universal ease. For productivity, it is a breakthrough. For security, it is a nightmare: every MCP connection is a new pipeline that bypasses your perimeter entirely.
A developer spins up an MCP server to let an AI agent query a customer database. That agent now has a direct, authenticated connection to sensitive data. It does not traverse your API gateway. It does not pass through your WAF. It just talks to the data, deep inside your network, in a conversation your security stack never sees.
Why Your WAF Is Watching the Wrong DoorHere is the uncomfortable truth: your WAF and API gateway were built for a world that no longer exists.
They analyze North-South traffic: requests coming in from the outside world. They are excellent at catching known attack signatures hitting your front door. But the Microsoft Copilot bug didn't come through the front door. It happened in the hallways.
East-West traffic: the lateral communication between microservices, AI agents, and data stores, is where the real risk lives now. Traditional perimeter tools are completely blind to it. The Copilot vulnerability wasn't a malicious payload; it was a context validation failure. No signature to detect. No anomaly at the edge. By the time anything could have been flagged, the data was already exposed.
Securing the Conversations You Can't SeeStopping these risks requires a fundamentally different approach: one that moves past perimeter defense and into the Agentic Action Layer where AI agents actually operate.
The takeaway from the Microsoft incident isn't just that Copilot had a bug. Bugs happen. The real takeaway is that the architecture of modern AI: agents operating deep inside trusted networks, consuming APIs at scale, making context decisions autonomously, has fundamentally broken the internal trust model.
Your most privileged users are no longer human. Your perimeter is a fiction. And the only defense that works is understanding intent.
Every API is now an edge API. The only question is whether you can see what is happening at that edge.
If you want to learn more about Salt and how we can help you, please contact us, schedule a demo, or visit our website. You can also get a free API Attack Surface Assessment from Salt Security's research team and learn what attackers already know.
The post Your Most Dangerous User Is Not Human: How AI Agents and MCP Servers Broke the Internal API Walled Garden appeared first on Security Boulevard.
Are Non-Human Identities the Key to Unlocking Agentic AI in Data Protection? Organizations across industries are increasingly focusing on the management of Non-Human Identities (NHIs). These machine identities, akin to digital passports, play a pivotal role in cybersecurity by managing encrypted passwords, tokens, and keys. Yet, how can NHIs serve as the cornerstone for Agentic […]
The post Why must healthcare embrace Agentic AI for data protection appeared first on Entro.
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How Secure Are Your Machine Identities? Non-Human Identities (NHIs) play a pivotal role in cybersecurity. Where businesses continue transitioning to cloud environments, the importance of protecting these machine identities becomes paramount. But how secure are your NHIs, and what measures are you implementing to ensure their protection? Understanding Non-Human Identities NHIs are essentially machine identities […]
The post What are the latest advancements in Non-Human Identity security appeared first on Entro.
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Is Your Organization Ready for Agentic AI in Cybersecurity? Where cyber threats are becoming increasingly sophisticated, the use of Agentic AI in cybersecurity is transforming how industries like financial services handle their security protocols. But what exactly does this mean for your organization’s cybersecurity strategy, especially when integrating Non-Human Identities (NHIs) into your security framework? […]
The post How can Agentic AI improve cybersecurity in financial services appeared first on Entro.
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How Can Organizations Leverage Non-Human Identities for Better Security? Have you ever wondered how Non-Human Identities (NHIs) are reshaping cybersecurity? With cyber threats evolve, organizations must prioritize the management of NHIs, especially when operating in cloud environments. NHIs serve as the machine identities within digital and are pivotal for modern security strategies. Understanding their role […]
The post How are new AI secrets vaulting methods empowering better security? appeared first on Entro.
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Session 12D: ML Backdoors
Authors, Creators & Presenters: Hanlei Zhang (Zhejiang University), Yijie Bai (Zhejiang University), Yanjiao Chen (Zhejiang University), Zhongming Ma (Zhejiang University), Wenyuan Xu (Zhejiang University)
PAPER
BARBIE: Robust Backdoor Detection Based On Latent Separability
Backdoor attacks are an essential risk to deep learning model sharing. Fundamentally, backdoored models are different from benign models considering latent separability, i.e., distinguishable differences in model latent representations. However, existing methods quantify latent separability by clustering latent representations or computing distances between latent representations, which are easy to be compromised by adaptive attacks. In this paper, we propose BARBIE, a backdoor detection approach that can pinpoint latent separability under adaptive backdoor attacks. To achieve this goal, we propose a new latent separability metric, named relative competition score (RCS), by characterizing the dominance of latent representations over model output, which is robust against various backdoor attacks and is hard to compromise. Without the need to access any benign or backdoored sample, we invert two sets of latent representations of each label, reflecting the normal latent representations of benign models and intensifying the abnormal ones of backdoored models, to calculate RCS. We compute a series of RCS-based indicators to comprehensively reflect the differences between backdoored models and benign models. We validate the effectiveness of BARBIE on more than 10,000 models on 4 datasets against 14 types of backdoor attacks, including the adaptive attacks against latent separability. Compared with 7 baselines, BARBIE improves the average true positive rate by 17.05% against source-agnostic attacks, 27.72% against source-specific attacks, 43.17% against sample-specific attacks and 11.48% against clean-label attacks. BARBIE also maintains lower false positive rates than baselines.
ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.
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Large language models (LLMs) are now embedded across the SDLC. They summarize documentation, generate code, explain vulnerabilities, and assist with architectural decisions.
The post Why LLMs Make Terrible Databases and Why That Matters for Trusted AI appeared first on Security Boulevard.
FRANKFURT, Feb. 19, 2026, CyberNewswire — Link11 launches its new “AI Management Dashboard”, closing a critical gap in how companies manage AI traffic. Artificial intelligence is fundamentally changing internet traffic. But while many companies are already feeling the … (more…)
The post News alert: Link11’s ‘AI Management Dashboard’ makes AI traffic, AI access policies enforceable first appeared on The Last Watchdog.
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The sky has never been falling. Yet here we are again, watching a new generation of prognosticators prophecy civilizational collapse while evidence of human adaptability and economic dynamism surrounds them. Salon’s recent piece about “swarms of AI bots threatening democracy” epitomizes this tiresome pattern—a sensational claim dressed up in legitimate-sounding language that dissolves upon even..
The post The Chicken Littles of Silicon Valley: Why AI Doomsayers Are Repeating History’s Greatest Mistake appeared first on Security Boulevard.
Frankfurt am Main, Germany, 19th February 2026, CyberNewswire
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Every security platform eventually faces the same foundational question: How should security rules be organized? At first glance, this sounds like a simple data-modeling choice. In practice, it defines the daily reality of security operations: how quickly incidents can be debugged, how safely policies can evolve, how easily new offices or user communities can be...
The post How Modern Security Platforms Organize Rules appeared first on Aryaka.
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Explore how AI agents enhance identity verification and authentication systems with smarter fraud detection and seamless user security.
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Originally published at Why White Label MSP Services Are Key to Growing Businesses by EasyDMARC.
Running a modern business means relying on technology ...
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Think of learner engagement in terms of the following dimensions: cognitive, emotional, and behavioral. Cognitive engagement refers to how students think about and process content. Emotional engagement involves their personal connection to learning. Behavioral engagement encompasses observable actions such as active participation in course material. When developing student engagement strategies, teachers must account for all ...
The post Learner Engagement in the Classroom: 5 Strategies appeared first on ManagedMethods Cybersecurity, Safety & Compliance for K-12.
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