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The Agentic Virus: How AI Agents Become Self-Spreading Malware
In my previous post, I walked through how disconnected MCP servers and AI agents create a growing blind spot in enterprise identity. The problem: thousands of MCP deployments running with overly broad tokens, no authentication, and no connection to your identity fabric. The solution: federate everything through the Maverics AI Identity Gateway. That post assumed...
The post The Agentic Virus: How AI Agents Become Self-Spreading Malware appeared first on Strata.io.
The post The Agentic Virus: How AI Agents Become Self-Spreading Malware appeared first on Security Boulevard.
The Epstein Files Reveal Stunning Operational Security Fails
The volume of information contained in the "Epstein Files," bizarre pictures they paint and our inability to know what they don't document complicate attempts to understand what it all means. What is clear is the digital detritus that can be generated by just a single iPad-using Boomer.
Cryptohack Roundup: 20 Years for $73 Million Scam
This week, a 20-year sentence in a $73 million scam, SafeMoon CEO got eight years for fraud, Sam Bankman-Fried sought a new trial, Epstein's early crypto investments, a U.K. lawsuit against HTX, a probe of a Trump-linked crypto deal, a crypto-linked home invasion and a $43 billion Bithumb error.
EU Privacy Watchdogs Pan Digital Omnibus
A slew of amendments to European tech regulations touted by the European Commission as necessary for boosting continental competitiveness is receiving pushback from privacy watchdogs unhappy with changes that could water down EU privacy laws.
NDSS 2025 – PBP: Post-Training Backdoor Purification For Malware Classifiers
Session 12B: Malware
Authors, Creators & Presenters: Dung Thuy Nguyen (Vanderbilt University), Ngoc N. Tran (Vanderbilt University), Taylor T. Johnson (Vanderbilt University), Kevin Leach (Vanderbilt University)
PAPER
PBP: Post-Training Backdoor Purification for Malware Classifiers
In recent years, the rise of machine learning (ML) in cybersecurity has brought new challenges, including the increasing threat of backdoor poisoning attacks on ML malware classifiers. These attacks aim to manipulate model behavior when provided with a particular input trigger. For instance, adversaries could inject malicious samples into public malware repositories, contaminating the training data and potentially misclassifying malware by the ML model. Current countermeasures predominantly focus on detecting poisoned samples by leveraging disagreements within the outputs of a diverse set of ensemble models on training data points. However, these methods are not applicable in scenarios involving ML-as-a-Service (MLaaS) or for users who seek to purify a backdoored model post-training. Addressing this scenario, we introduce PBP, a post-training defense for malware classifiers that mitigates various types of backdoor embeddings without assuming any specific backdoor embedding mechanism. Our method exploits the influence of backdoor attacks on the activation distribution of neural networks, independent of the trigger-embedding method. In the presence of a backdoor attack, the activation distribution of each layer is distorted into a mixture of distributions. By regulating the statistics of the batch normalization layers, we can guide a backdoored model to perform similarly to a clean one. Our method demonstrates substantial advantages over several state-of-the-art methods, as evidenced by experiments on two datasets, two types of backdoor methods, and various attack configurations. Our experiments showcase that PBP can mitigate even the SOTA backdoor attacks for malware classifiers, e.g., Jigsaw Puzzle, which was previously demonstrated to be stealthy against existing backdoor defenses. Notably, your approach requires only a small portion of the training data -- only 1% -- to purify the backdoor and reduce the attack success rate from 100% to almost 0%, a 100-fold improvement over the baseline methods.
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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Атомная станция на Луне? Звучит как фантастика, но в США уже греют топливо
Unity游戏逆向实战 - Mono打包方式完全解析
Romania's oil pipeline operator Conpet confirms data stolen in attack
The Gentleman
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MSP Strategic Defense: Where Prevention Meets Compliance
Imagine a modern office building. Not everyone who works there can go everywhere. Employees can access the building entrance, their own floor, and the meeting rooms they need, but they can’t (and shouldn’t be able to) walk into the server room, access executive offices, or wander freely across every floor. This may seem restrictive, but it’s simply how safety and order are maintained.
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4 Tools That Help Students Focus
Educators recognize the dual reality of educational technology (EdTech): its potential to sharpen student focus and detract from it. Schools must proactively leverage technology’s advantages while mitigating its risks to student productivity. Read on as we unpack the evolving importance and challenge of supporting student focus. We also detail four categories of classroom focus tools, ...
The post 4 Tools That Help Students Focus appeared first on ManagedMethods Cybersecurity, Safety & Compliance for K-12.
The post 4 Tools That Help Students Focus appeared first on Security Boulevard.
Четыре ускорителя и 32 спутника. Европа запускает самую мощную версию ракеты Ariane 6
Dutch mobile phone giant Odido announces data breach
Odido data breach exposes personal info of 6.2 million customers
CVE-2026-24135
CVE-2025-64111
CVE-2025-1974
DragonForce
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INC
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