Featured Issues

Moving up the Cyber Kill ChainNeed something to read?Develop foundational skills in ethical hacking and penetration testing while getting ready to pass the certification exam. With cyber threats continually evolving, understanding the trends and using the tools deployed by attackers to determine vulnerabilities in your system can help secure your applications, networks, and devices. To outmatch attacks, developing an attacker's mindset is a necessary skill, which you can hone with the help of the Certified Ethical Hacker 312-50 Exam Guide.- Learn how to look at technology from the standpoint of an attacker- Understand the methods that attackers use to infiltrate networks- Prepare to take and pass the exam in one attempt with the help of hands-on examples and mock testsCheck it out today!#209: Stopping Malicious InstallationA look at the issuesWelcome to another_secpro!This week, we're looking at installation on CKC, reflecting on the week's biggest stories, and heading out into the quagmire of modern academia. Sound good? Scroll down and check out what we have on offer.Check out _secpro premiumIf you want more, you know what you need to do: sign up to the premium and get access to everything we have on offer. Click the link above to visit our Substack and sign up there!Cheers!Austin MillerEditor-in-ChiefSpeed up mobile dev - sign up today!Webinar alert! Mobile experts from Bitrise and Embrace break down advanced CI/CD tips and real-user insights to help you speed up builds & deliver top-quality apps. Register here.Register now!This week's articlesInstallation in the Cyber Kill ChainIf you have worked through the more dangerous stages of a cyberattack, you probably know there’s a method to the adversarial madness. Attackers follow a loose sequence of steps to get from “target spotted” to “data exfiltrated” or “system held hostage.” The Cyber Kill Chain is, obviously, one way of describing this sequence. It’s not a law of nature, and attackers don’t always follow it like a checklist, but it helps analysts break down and understand what’s happening.Read the rest here!News BytesMicrosoft Investigates Active Leak as SharePoint Zero‑Days Exploited for Warlock Ransomware: Microsoft is probing whether details from its Microsoft Active Protections Program (MAPP) were leaked—possibly by trusted participants—allowing Chinese-linked threat groups (Linen Typhoon, Violet Typhoon, Storm‑2603) to exploit zero‑day vulnerabilities in SharePoint. Although patches were issued in July, evidence suggests hackers began attacks soon after, raising concerns about program integrity.Chinese‑Linked Groups Target U.S. Agencies via SharePoint Zero‑Day Campaign: A sophisticated global cyber‑espionage campaign has compromised nearly 100 organizations—including U.S. federal agencies—through SharePoint on‑prem installations. The campaign is attributed to Chinese-affiliated groups and highlights the risks of self-hosted deployments not covered by Microsoft’s cloud protections.Storm‑2603 Deploys DNS‑Controlled AK47 C2 in Warlock and LockBit Campaigns: Researchers observed the threat actor Storm‑2603 using a DNS‑based command‑and‑control framework named AK47DNS (alongside AK47HTTP) to deploy Warlock and LockBit ransomware. The group has been active since March 2025, combining multiple ransomware families in coordinated campaigns. Scattered Spider Ransomware Ring: Four Arrests in U.K. Retail and Airline Attacks: Four individuals (ages 17–20) were arrested in the U.K. for data theft and extortion targeting retailers like Harrods, Marks & Spencer, and Co‑op Group. The scammers, known as “Scattered Spider,” specialize in social‑engineering attacks via help desks, impersonation, and air‑gapped access.ATM Network Breached Using 4G‑Connected Raspberry Pi and Kernel Rootkit:Threat actors (UNC2891) planted a Raspberry Pi connected via 4G and installed the CAKETAP kernel rootkit to spoof ATM hardware authentication. By hiding processes and intercepting card verification messages, they bypassed standard forensic detection.British Transport Firm KNP Shuts Down After Ransomware via Weak Password Guessing: U.K. railway company KNP (158 years old) was victim to an Akira ransomware attack that began with a single weak employee password. A £5 million ransom demand followed; the company’s infrastructure was encrypted and the crisis unmanaged, leading to company closure and 700 job losses.This week's academiaNeuromorphic Mimicry Attacks Exploiting Brain‑Inspired Computing for Covert Cyber Intrusions(H. Ravipati): This pioneering work describes a novel class of cyber‑attacks—"Neuromorphic Mimicry Attacks" (NMAs)—targeting neuromorphic chips (brain‑inspired hardware) used in edge/AI devices. By subtly tampering with synaptic weights or poisoning sensory inputs, attackers mimic legitimate neural activity and evade conventional intrusion detection. The paper proposes anomaly detection tailored to neural behavior and secure synaptic learning protocols. It’s attracting attention because as neuromorphic hardware becomes mainstream (e.g. for smart sensors, medical implants, autonomous systems), this attack vector could emerge rapidly in coming years.From Texts to Shields: Convergence of Large Language Models and Cybersecurity (Tao Li, Ya‑Ting Yang, Yunian Pan, Quanyan Zhu): This interdisciplinary report explores how large language models (LLMs) are both empowering and challenging cybersecurity. It surveys applications like automated vulnerability analysis (e.g. 5G code scanning), generative security tooling, and network threat detection—while also examining socio‑technical concerns around trust, transparency, and fairness. It proposes human‑in‑the‑loop workflows, interpretability mechanisms, and proactive robustness testing. The paper has gained traction due to the surging integration of LLMs in defense tools and Widespread debate around safe deployment.Red Teaming with Artificial Intelligence‑Driven Cyberattacks: A Scoping Review: (M. Al‑Azzawi, Dung Doan, T. Sipola, J. Hautamäki, T. Kokkonen): This scoping review examines how artificial intelligence can automate red‑team cyberattacks, from reconnaissance to exploit deployment. Analysing nearly 500 studies (11 included in the final review), it categorizes AI methods used to breach systems, extract data, and manipulate targets. The paper warns that AI‑powered adversarial tools are lowering the barrier for sophisticated attacks—and calls for defensive capabilities that also leverage AI to keep pace. It’s widely cited in discussions about where adversarial and defensive AI arms races are headed.Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey (Shuang Tian, Tao Zhang, Jiqiang Liu, Jiacheng Wang, et al.): This systematic survey examines use cases of LLMs across the cyber‑attack lifecycle—from reconnaissance to lateral movement and threat intelligence. It highlights LLMs’ strengths in pattern recognition, automated exploration of network configurations, and real‑time response generation, while also covering associated risks—such as prompt injection, hallucinations, data leakage, and adversarial manipulation. The paper is timely, aligning with current concerns about prompt‑injection (now classified as a top risk in OWASP’s 2025 LLM Top 10).Upcoming events for _secpros this yearHere are the five conferences we're looking forward to the most this year (in no particular order...) and how you can get involved to boost your posture!DSEI (9th-12th September): DSEI stands out as a global platform that bridges defence, security, and cybersecurity. With its broad focus on cutting-edge technologies, this event is critical for those involved in national defence, law enforcement, and private security. Cybersecurity is a prominent theme, with sessions addressing both offensive and defensive cyber strategies.Defcon (7th-10th August): Defcon is a legendary event in the hacker and cybersecurity communities. Known for its hands-on approach, Defcon offers interactive workshops, capture-the-flag contests, and discussions on emerging threats. The conference is ideal for those looking to immerse themselves in technical aspects of cybersecurity.Black Hat (2nd-7th August): Black Hat USA is synonymous with advanced security training and research. This premier event features technical briefings, hands-on workshops, and sessions led by global security experts. Attendees can explore the latest trends in penetration testing, malware analysis, and defensive techniques, making it a must-attend for cybersecurity professionals.Check out the survey!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Extract, orchestrate, and act: the future of AI systems is proactive. AI_Distilled #106: What’s New in AI This Week DeepSeek In Production DeepSeek is fast becoming the open-source LLM of choice for developers and engineers focused on speed, efficiency, and control. Join "DeepSeek in Production" summit to see how Experts are fine-tuning DeepSeek for real-world use cases, building agentic workflows, and deploying at scale. Seats are filling fast. Limited slots left. Book now at 30% off. SECURE YOUR SPOT NOW! Apply codeDEEPSEEK30 at checkout to avail your 30% off (Expires on Sun, Aug 3). This week, AI is dominating headlines. OpenAI's GPT-5 is launching, enhancing Microsoft Copilot and ChatGPT's Study Mode. Meta's stock surged on AI investments, while Tesla's $16.5 billion chip deal boosts Samsung. Gulf nations are betting big on AI as their "new oil," showcasing AI's transformative impact across industries and global economies, creating a stark contrast with struggling consumer businesses. Want to find out more? Let’s dig in! LLM Expert Insights, Packt In today's issue: 🧠 Expert Deep Dive: Denis Rothman reveals how to build AI systems that think for themselves—using GPT-4o and GenAISys for autonomous task orchestration. 📅 August AI Event Watch: DeepSeek in Production and other major events like Ai4, KDD, and AI Risk Summit spotlight GenAI systems, risk, and real-world deployments. 🧪 OpenAI’s GPT-5 Set to Launch: Coming in August—GPT-5 will power smarter Copilot experiences and debut mini/nano variants for edge and enterprise AI. 📘 ChatGPT Gets Study Mode: A new interactive learning mode focuses on guided problem solving and deeper understanding—backed by educators and scientists. 🤝 Tesla & Samsung Ink $16.5B Deal: Samsung to manufacture Tesla’s next-gen AI6 chips at its Texas factory—reviving its foundry business. 📈 Meta’s AI Bet Pays Off: With big infra investments and a “superintelligence” vision, Meta sees an 11% stock rally and renewed investor confidence. 📖 Expert Insights Recap: Learn how to build AI controllers that proactively match vague user intent to the right task—without needing explicit prompts. 📈UPCOMING EVENTS In addition to Packt’s event on August 16th — DeepSeek in Production — here’s a curated list of major AI and LLM-focused events taking place this month. Generative AI Summit LA 2025 Date: August 27, 2025 Location: Los Angeles, California, USA Format: In person Cost: $295 (Normal pass) and $595 (VIP pass). Visit for more details. KDD 2025 — ACM SIGKDD Conference on Knowledge Discovery & Data Mining Date: August 37, 2025 Location: Toronto, Canada Format: In person (with potential virtual access) Cost: $600 (One-day pass) to $1,750 (Full conference; based on membership). Visitfor fee details. Ai4 2025 Date: August 1113, 2025 Location: Las Vegas, Nevada, USA Format: In person (enterprisefocused) Cost: $3,195 (Standard pass; early discounted rate: $2,595), $5,595 (VIP pass; early discounted rate: $4,495). Visit for more details. AI Risk Summit 2025 Date: August 1920, 2025 Location: Half Moon Bay, California, USA Format: In person Cost: $1,995. Visit for registration and pricing. Upskilling with MCP and A2A protocols is your gateway to building AI agents. Don’t miss the chance to explore these events and get ahead. EXPERT INSIGHTS From the Packt Bookshelf: Building Business-Ready Generative AI Systems by Denis Rothman In the rapidly evolving landscape of generative AI, creating truly autonomous systems requires thoughtful orchestration and sophisticated intent recognition. In this exclusive excerpt from his recent book, Building Business-Ready Generative AI Systems, AI expert and visionary Denis Rothman shares a practical approach for designing AI controllers that proactively select and execute tasks without explicit instruction. Denis explains how leveraging GPT-4o’s semantic analysis capabilities allows your AI system to intelligently match vague or implicit user inputs to the correct action, be it sentiment analysis or semantic search. Here’s a peek under the hood of how that works. Selecting a scenario The core of an AI controller is to decide what to do when it receives an input (system or human user). The selection of a task opens a world of possible methods that we will explore throughout the book. However, we can classify them into two categories: Using an explicit task tag to trigger an instruction. This tag can be a context in a generative AI model and expressed freely in various ways in a prompt. The prompt has no task instruction but instead a repository of scenarios from which the AI controller will make decisions based on semantic textual similarity. Here, we’ll explore the second, more proactive approach. We’ll test two prompts with no instructions, no task tag, and no clue as to what is expected of the generative AI model. Although we will implement other, more explicit approaches later with task tags, a GenAISys AI controller orchestrator must be able to be proactive in certain situations. The first prompt is an opinion on a movie, implying that a sentiment analysis might interest the user: if prompt==1: input = "Gladiator II is a great movie although I didn't like some of the scenes. I liked the actors though. Overall I really enjoyed the experience." The second prompt is a fact, implying that a semantic analysis might interest the user: if prompt==2: input = "Generative AI models such as GPT-4o can be built into Generative AI Systems. Provide more information." To provide the AI controller with decision-making capabilities, we will need a repository of instruction scenarios. Defining task/instruction scenarios Scenarios are sets of instructions that live in a repository within a GenAISys. While ChatGPT-like models are trained to process many instructions natively, domain-specific use cases need custom scenarios (we’ll dive into these starting from Chapter 5). For example, a GenAISys could receive a message such as Customer order #9283444 is late. The message could be about a production delay or a delivery delay. By examining the sender’s username and group (production or delivery department), the AI controller can determine the context and, selecting a scenario, take an appropriate decision. In this notebook, the scenarios are stored in memory. In Chapter 3, we will organize the storage and retrieval of these instruction sets in Pinecone vector stores. In both cases, we begin by creating a repository of structured scenarios (market, sentiment, and semantic analysis): scenarios = [ { "scenario_number": 1, "description": "Market Semantic analysis.You will be provided with a market survey on a give range of products.The term market must be in the user or system input. Your task is provide an analysis." }, { "scenario_number": 2, "description": " Sentiment analysis Read the content and classify the content as an opinion If it is not opinion, stop there If it is an opinion then your task is to perform a sentiment analysis on these statements and provide a score with the label: Analysis score: followed by a numerical value between 0 and 1 with no + or - sign.Add an explanation." }, { "scenario_number": 3, "description": "Semantic analysis.This is not an analysis but a semantic search. Provide more information on the topic." } ] We will also add a dictionary of the same scenarios, containing simple definitions of the scenarios: # Original list of dictionaries scenario_instructions = [ { "Market Semantic analysis.You will be provided with a market survey on a give range of products.The term market must be in the user or system input. Your task is provide an analysis." }, { "Sentiment analysis Read the content return a sentiment analysis on this text and provide a score with the label named : Sentiment analysis score followed by a numerical value between 0 and 1 with no + or - sign and add an explanation to justify the score." }, { "Semantic analysis.This is not an analysis but a semantic search. Provide more information on the topic." } ] We now extract the strings from the dictionary and store them in a list: # Extract the strings from each dictionary instructions_as_strings = [ list(entry)[0] for entry in scenario_instructions ] At this point, our AI controller has everything it needs to recognize intent—matching any incoming prompt to the best-fitting scenario. Performing intent recognition and scenario selection We first define the parameters of the conversational AI agent just as we did in the Conversational AI agent section: # Define the parameters for the function call mrole = "system" mcontent = "You are an assistant that matches user inputs to predefined scenarios. Select the scenario that best matches the input. Respond with the scenario_number only." user_role = "user The orchestrator’s job is to find the best task for any given input, making the AI controller flexible and adaptive. In some cases, the orchestrator may decide not to apply a scenario and just follow the user’s input. In the following example, however, the orchestrator will select a scenario and apply it. We now adjust the input to take the orchestrator’s request into account: # Adjust `input` to combine user input with scenarios selection_input = f"User input: {input}\nScenarios: {scenarios}" print(selection_input) GPT-4o will now perform a text semantic similarity search as we ran in the Semantic Textual Similarity Benchmark (STSB) section. In this case, it doesn’t just perform a plain text comparison, but matches one text (the user input) against a list of texts (our scenario descriptions): # Call the function using your standard API call response = openai_api.make_openai_api_call( selection_input, mrole, mcontent, user_role ) Our user input is as follows: User input: Gladiator II is a great movie Then, the scenario is chosen: # Print the response print("Scenario:",response ) The scenario number is then chosen, stored with the instructions that go with it, and displayed: scenario_number=int(response) instructions=scenario_instructions[scenario_number-1] print(instructions) For our Gladiator II example, the orchestrator correctly picks the sentiment analysis scenario: {'Sentiment analysis Read the content return a sentiment analysis on this text and provide a score with the label named : Sentiment analysis score followed by a numerical value between 0 and 1 with no + or - sign and add an explanation to justify the score.'} This autonomous task-selection capability—letting GenAISys choose the right analysis without explicit tags—will prove invaluable in real-world deployments (see Chapter 5). The program now runs the scenarios with the generative AI agent. This kind of proactive orchestration is foundational to building truly agentic systems that don’t wait to be told exactly what to do but instead figure it out based on context. The full book goes much deeper, covering vector search, scenario libraries, custom instruction sets, and multi-user memory. If you're ready to level up from simple chatbots to intelligent workflows that think for themselves, Building Business-Ready Generative AI Systems by Denis Rothman is your blueprint. Liked the Insights? Want to dig in deeper? Build Human-Centered Generative AI Systems with Agents, Memory, and LLMs for Enterprise Build an adaptive, context-aware AI controller with advanced memory strategies Enhance GenAISys with multi-domain, multimodal reasoning capabilities and Chain of Thought (CoT) Seamlessly integrate cutting-edge OpenAI and DeepSeek models as you see fit BUY NOW 📈LATEST DEVELOPMENT Here is the news of the week: OpenAI's GPT-5 Set for August Launch OpenAI's GPT-5 is reportedly launching in August. CEO Sam Altman has teased its advanced capabilities, demonstrating its ability to answer complex questions instantly. The new model will also include "mini" and "nano" versions and integrate reasoning capabilities, marking a significant leap for the AI giant. Meanwhile, Microsoft's Copilot is set to receive OpenAI's GPT-5 model, likely alongside ChatGPT, with a new "Smart" chat mode identified. Expected in August, GPT-5 will enhance Copilot's ability to adapt its response strategy automatically, streamlining user experience. This integration underscores Microsoft's commitment to positioning Copilot as a leading, user-friendly AI assistant. ChatGPT Introduces "Study Mode" for Deeper Learning ChatGPT has launched "Study Mode," a new feature designed to provide step-by-step guidance for learning instead of instant answers. Available to most users, this mode uses interactive prompts, scaffolded responses, and personalized support to encourage critical thinking and deeper understanding. Built with input from educators and scientists, "Study Mode" aims to help students truly learn material for homework, test prep, and new concepts. Tesla's $16.5 Billion Chip Deal Boosts Samsung's US Foundry Business Tesla has inked a $16.5 billion deal with Samsung Electronics for its next-generation AI6 chips, to be produced at Samsung's new Taylor, Texas factory. This significant contract is a major win for Samsung's struggling foundry business, helping secure a key client and boosting its shares by 6.8%. While unlikely to immediately impact Tesla's EV sales or robotaxi rollout, the deal is crucial for Samsung's goal to expand its contract chip manufacturing and compete with rivals like TSMC. AI Investments Fuel Meta's Revenue Growth and Stock Rally Meta's stock surged 11% after a strong Q3 revenue forecast. This was largely fueled by significant AI investments. CEO Mark Zuckerberg's memo detailed ambitious plans for "superintelligence." He noted visible improvements in their AI systems. This massive spending spree, including poaching talent, acquiring startups, and building data centers, is paying off. These efforts are reassuring investors and driving substantial stock gains. Built something cool? Tell us. Whether it's a scrappy prototype or a production-grade agent, we want to hear how you're putting generative AI to work. Drop us your story at nimishad@packtpub.com or reply to this email, and you could get featured in an upcoming issue of AI_Distilled. 📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us. If you have any comments or feedback, just reply back to this email. Thanks for reading and have a great day! That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️ We would love to know what you thought—your feedback helps us keep leveling up. 👉 Drop your rating here Thanks for reading, The AI_Distilled Team (Curated by humans. Powered by curiosity.) *{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}