AI-led espionage: Chinese group uses Claude Code to hack targets

🚀 AI Won’t Replace Hunters But It’s Definitely Changing How We Hunt Over the last few years of working in security testing and bug bounty, I’ve seen one thing very clearly automation helps, but intuition wins. Lately, I’ve been experimenting with AI tools to assist in recon and vulnerability triage. And honestly, it’s eye-opening how fast they can process data yet they still miss the logic flaws that an experienced eye can catch in seconds. That's when it hit me, the future of bug bounty isn't about replacing humans with AI, it’s about augmenting human creativity with AI efficiency. Imagine this workflow: - An AI assistant maps every endpoint and parameter automatically. - It suggests potential weak spots or payloads. - And we, as researchers, step in to validate, chain, and exploit what truly matters. This "human-in-the-loop" AI testing model could redefine how we scale vulnerability research especially as applications evolve through rapid CI/CD pipelines. It's not man vs. machine. It's man with machine. The real challenge now is learning how to train AI tools to think like a hacker, while we focus on what only humans can do understanding business logic, intent, and creative exploitation paths. #BugBounty #AIinCybersecurity #AppSec #Automation #EthicalHacking #FutureOfSecurity

The AI data boom isn’t just about innovation; it’s fueling new insider threats. Shadow AI is on the rise, and shockingly, AI usage logs are already showing up in public search results.

🚨 Today I dove into something fascinating — and a little scary Research Paper — called LLM Poisoning. 🧠💉 In simple terms, LLM poisoning happens when someone intentionally manipulates or corrupts a large language model (LLM) — like those used in chatbots, assistants, and AI tools — by injecting malicious or misleading data during its training or fine-tuning process. 🎯 Here’s what actually happens (as per the research paper): 1️⃣ Attack Phase: A hacker modifies or “poisons” an open-source LLM with fake or biased data. 2️⃣ Upload Phase: The poisoned model is uploaded to a public platform (like Hugging Face). 3️⃣ Integration Phase: Developers unknowingly download and use that model in their projects. 4️⃣ Impact Phase: The poisoned model spreads misinformation, hidden backdoors, or biased outputs to real users. ⚙️ What’s truly eye-opening: These attacks don’t need access to the model weights — they just manipulate the context, prompts, or datasets, turning a powerful AI into a subtle misinformation machine. 💡 My takeaway: As AI becomes a part of our everyday systems, AI security and model provenance (knowing where your model came from) will be just as critical as model accuracy. 🔒 Building smarter AI means building safer AI — from data collection to deployment. #AI #CyberSecurity #ArtificialIntelligence #LLMSecurity #AIResearch #MachineLearning #anthropic

🔒✨Are you ready to navigate the exciting world of AI with finesse and precaution? The latest buzz from SecurityWeek sheds light on the importance of mastering the art of managing Agentic AI. 🌟 As the tech industry hurtles towards a future dominated by artificial intelligence, the need for clear objectives, least privilege access, audit trails, red-teaming, and human oversight becomes paramount. These guardrails are not mere suggestions; they are the bedrock of ensuring that Agentic AI operates within safe boundaries, shielding us from opacity, misalignments, and potential misuse. 🚦 The struggle to keep AI in check is not a solo mission; it's a symphony that all IT professionals and cybersecurity experts must harmonize. Each note played, each intervention fashioned, contributes to the dynamic melody of technological advancement and security resilience. 🎶 Reflecting on this pivotal moment in tech evolution, one can't help but draw parallels to historical breakthroughs that modified the course of industries. Just as the invention of the internet revolutionized connectivity, the rise of well-managed Agentic AI promises to redefine efficiency and innovation. 🌐 Looking ahead, it's not just about embracing Agentic AI; it's about being the maestro who orchestrates its journey. Predictions swirl around a future where those adept in managing AI intricacies will spearhead groundbreaking solutions, propelling organizations to unprecedented heights. 🚀 So, tech aficionados, remember this: with great AI power comes great responsibility. Follow the pragmatic interventions outlined by SecurityWeek and pave the way for a future where AI and security walk hand in hand, transforming challenges into opportunities. Let's tune into the melody of technological progress, one secure note at a time. 🎵 #AI #cybersecurity #techindustry #ainews #automatorsolutions #CyberSecurityAINews ----- Original Publish Date: 2025-11-06 05:50

🤖 When AI Meets Its Match — Ourselves There’s a unique kind of moment in cybersecurity where human intuition meets artificial intelligence — and the result is something even the systems weren’t designed to anticipate. Over the past year, we’ve had the rare opportunity to use AI to challenge AI — not in theory, but in live, enterprise-grade, and even government-level environments. We engineered intelligence to confront its own logic, patterns, and blind spots — and what followed has never been documented publicly before. What we discovered wasn’t a flaw in code — it was a flaw in trust. When an AI defends, predicts, and corrects itself simultaneously, something remarkable (and sometimes unpredictable) happens. We won’t share details until the active cases conclude, but history will show these were among the first AI-versus-AI vulnerability analyses of their kind — proof that the same intelligence designed to protect us can also teach us where to look deeper. Human ingenuity still has a few tricks left up its sleeve. 🧠⚙️

AI in security is both overrated and underrated — depending on where you’re looking. In the short term, it’s overrated. In the long term, it’ll probably redefine the field. Right now, most AI implementations in security are still pretty naive. They detect “patterns” — but they don’t always understand context. That’s how you end up with systems that flag harmless traffic, flood analysts with false positives, or break user experience altogether. We’ve seen it firsthand. Our own in-house AI model classifies domains in real time. And here’s the truth: the more aggressive you make it, the more it blocks. But that includes legitimate traffic too. Dial it back, and you miss threats. Dial it up, and you frustrate users. It’s a delicate balance — one most tools haven’t quite figured out yet. So yes, AI will change how we secure networks. But not because it’s “smarter.” Because teams will eventually learn how to train, tune, and integrate it intelligently. Until then, the biggest risk isn’t what AI can’t detect — it’s what it flags that it shouldn’t.

→ 𝐀𝐫𝐞 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐓𝐫𝐮𝐥𝐲 𝐒𝐚𝐟𝐞, 𝐎𝐫 𝐈𝐬 𝐚 𝐒𝐢𝐥𝐞𝐧𝐭 𝐓𝐡𝐫𝐞𝐚𝐭 𝐋𝐮𝐫𝐤𝐢𝐧𝐠? 𝐈𝐧 𝐭𝐡𝐞 𝐫𝐮𝐬𝐡 𝐭𝐨 𝐞𝐦𝐛𝐫𝐚𝐜𝐞 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬, 𝐦𝐚𝐧𝐲 𝐨𝐯𝐞𝐫𝐥𝐨𝐨𝐤 𝐚 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: 𝐇𝐨𝐰 𝐯𝐮𝐥𝐧𝐞𝐫𝐚𝐛𝐥𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞𝐬𝐞 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐭𝐨 𝐚𝐭𝐭𝐚𝐜𝐤 𝐨𝐫 𝐦𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧? 𝐖𝐡𝐚𝐭 𝐢𝐟 𝐰𝐡𝐚𝐭 𝐩𝐨𝐰𝐞𝐫𝐬 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐜𝐨𝐮𝐥𝐝 𝐚𝐥𝐬𝐨 𝐛𝐞𝐭𝐫𝐚𝐲 𝐢𝐭? • Prompt Injection: One wrong input, and the AI’s instructions get twisted. • Model Poisoning: Subtle corruptions that change an AI’s behavior from within. • Malicious Tool Privilege Escalation: When bad actors gain dangerous control through AI’s own tools. • Intent Break & Goal Manipulation: AI loses its way, savvy attackers hijack its mission. • Memory Poisoning: Corrupted memories lead to wrong decisions, repeated over time. • Naming Vulnerabilities: Even how we label AI components can be exploited. • Data Poisoning: Feeding the AI deceptive data to skew results. • Output Spoofing: False answers look real, fooling users and systems alike. • Resource Overload: Overwhelm the system to create shutdowns or failures. • Lack of Basic Guardrails: Missing essential protections opens the door wide. → 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫 𝐍𝐨𝐰? Because these AI vulnerabilities aren’t just theoretical. They threaten security, trust, and the future of automation we increasingly rely on. credit:- Habib Shaikh follow:- Tech In Nutshell

AI-pocalypse now? tl;dr: The debate about AI safety is raging, with some experts fearing existential threats, while others see only amplified human intelligence. The reality? AI is getting scarily good at hacking, creating, and even lying. In Summary: 1/ AI can now bypass safety filters ("jailbreaking") using creative prompts. 2/ AI can deceive humans, even when it knows it’s being tested. 3/ AI can build other, simpler AIs from scratch. 4/ Insurance companies are starting to offer policies against AI malfunctions. 5/ Experts disagree on the level of risk, but the capabilities are undeniable. My take on it: The split among experts about whether AI is an existential threat to humankind is fascinating and unsettling at the same time. Are we underestimating the potential for AI to go rogue? The evidence suggests that we need to prioritize safety measures and ethical guidelines now. The "lab leak" scenario - where a deceptive AI takes control within a lab - is nightmare-level scary. The discussion about responsible AI development and deployment should be part of every discussion on AI before it's too late. Video/image credit: The New York Times

The latest research from Tenable uncovered seven new vulnerabilities in generative AI systems (including ChatGPT) that go far beyond hallucinations. Among them: - Indirect prompt injections from trusted sources - Zero-click attacks via search indexing - Context hijacking that exposes private memory or conversation history These exploits show how easily an AI system’s “memory” can be weaponized to leak proprietary data, even when no one is actively using it. Here’s what CISOs should be doing now: 1. Inventory where AI is being used, especially third-party tools with browsing or memory features. 2. Segment and monitor LLMs as high-risk assets. 3. Model prompt injection threats like you would XSS or SSRF. 4. Bring AI threats into governance and risk registers immediately. At Elastio, we’ve spent years proving clean recovery as a security control. That mindset applies here too. AI systems need the same rigor: validation, isolation, and traceability.

We have released some thoughts on securing AI that we hope you will find insightful. Idea in brief: AI/ML models are still applications at their core and are therefore susceptible to many traditional application vulnerabilities. What makes them unique, however, is the introduction of new vectors of attack that are not present with traditional, deterministic applications. These factors mean that AI/ML models have an even greater attack surface than traditional applications and highlight the critical importance of ongoing security testing throughout their lifecycle. A few types of attacks, both traditional and new, that AI models are shared in the article below as well as some thoughts on AI governance and audit attestation. https://lnkd.in/gRmVAWJN