OWASP GenAI Security Project Top 10 for Agentic Apps

🧠💻 AI-Generated Code = Faster Development, Higher Risk? Recent research highlighted by SoftwareSeni shows that AI-generated code contains 2.74x more security vulnerabilities than human-written code. Some key findings: ⚠️ 45% of AI code includes OWASP Top 10 risks 🔓 322% more privilege escalation paths 🔑 40% increase in exposed secrets 🧩 153% more design-level security flaws The takeaway is simple: AI coding tools are productivity accelerators — not security tools. To use them safely, teams need: ✅ SAST/DAST scanning in CI/CD ✅ Secrets detection (pre-commit) ✅ Mandatory security reviews (especially auth & access control) ✅ Clear security requirements before generating code As AI becomes part of every dev workflow, the mindset should be: 👉 Treat AI-generated code as untrusted input. James A. Wondrasek - softwareseni https://lnkd.in/gizjJvUw. #cybersecurity #AI #softwaredevelopment #infosec #DevSecOps

🚨 This wasn’t a hack… but it might be more important than one. Anthropic accidentally leaked internal code from its Claude Code CLI on March 31, 2026. No hacking. No data breaches. No stolen user information. Just one tiny mistake 👇 A .map file in an npm package. Which allowed individuals to reverse-engineer 500,000+ internal code lines. 🤯 Why this is a BIG deal This isn’t a leak… This is a sneak peek into how vulnerable AI infrastructures are becoming. With just one file, individuals can: Understand internal agent architectures. Reverse engineer internal workflows. Uncover hidden features. Clone and exploit AI tools much quicker. 🧠 What shocked the developer community The leaked code, according to developers, reveals: Always-online AI agents running in the background. Multi-agent coordination systems. Deep planning features, such as 30-minute “thinking” time. Experimental features not even publicly released. ⚠️ Note: The accuracy of these claims has not been fully verified, but the direction they take us in is. AI has evolved from just a “chatbot” to… Something much, much bigger. ⚠️ The actual danger here isn’t… The leak itself. But what comes next: Fake npm packages. Malware masquerading as AI tools. Easier reverse engineering of AI tools. Eroding trust in developer tools. Supply chain risk + AI + npm = new attack surface. However, we're still securing AI like a regular app.The gap here is a problem.🔥 Hot take: The biggest risk in AI right now… Isn’t even AI models. It’s how we build, ship, and trust tools around them. #AISecurity #CyberSecurity #ArtificialIntelligence #DevTools

Why is domain knowledge the ultimate cheat code in the AI era? 🤔 It’s not about how fast you can prompt new tools. It’s about knowing when the AI is confidently wrong. Let’s talk about security reviews. 🛡️ Everyone is praising Claude’s ability to audit codebases. So, I ran an experiment on a small tool with zero prior security reviews. The specs: 1️⃣ 26 third-party components 2️⃣ 6,000+ lines of code From my domain expertise, I knew there were issues. But what did Claude find? I connected the GitHub repo and ran /security-review. Claude’s verdict: "All three false-positive filter agents returned confidence scores of 2/10, well below the required threshold of 8." It basically told me to move on with my life—with total authority! 🛑 But with just a 5-second glance, I knew this was AI word salad. If I didn’t have the domain expertise, I could have easily trusted it and shipped the code. The hard truth? There was a significant Remote Code Execution (RCE) vulnerability sitting right there. 💥 AI can help you accomplish tasks in a fraction of the time, but speed doesn't guarantee reliability. When have you caught AI making a dangerous mistake in your field? Share your story below! 👇 #ArtificialIntelligence #CyberSecurity #TechLeadership

The Unchecked Rise of Personal AI Agents: A Cybersecurity Powder Keg + Video Introduction: The proliferation of personal AI agents marks a paradigm shift in human-computer interaction, but beneath the veneer of convenience lies a burgeoning attack surface. As these autonomous entities integrate with highly privileged tools, external data streams, and dynamically evolving ecosystems, they inadvertently become prime vectors for exploitation, turning routine automation into a critical security liability. Learning Objectives: Understand the architectural risks associated with integrating AI agents into privileged environments....

Anthropic recently patched critical security vulnerabilities in the Model Context Protocol (MCP) servers. The Model Context Protocol is the industry standard bridge that allows AI models to connect directly to your local files, databases, and internal enterprise tools. It is the plumbing that makes AI agents useful in a professional environment. Security researchers found an exploit chain that allowed for Remote Code Execution (RCE). An attacker could potentially run commands on a developer's machine by tricking the AI into reading a malicious project file. This is a significant risk for any team currently using AI to automate local workflows. While open standards like MCP are necessary for scaling AI, they create new points of entry for attackers. Secure infrastructure is a requirement for any real-world AI deployment. You can protect your local environment by taking these direct actions: • Update your Git and Filesystem MCP servers to the latest patched versions immediately. • Use the principle of least privilege by restricting AI access to only the specific folders and data it needs. • Implement human-in-the-loop approvals for any AI action that involves writing files or executing code. • Audit your session logs to look for unusual tool calls or unexpected data requests. • Only use MCP servers from trusted, well-maintained sources. A secure connection is the only way to get the benefits of AI without exposing your private data. How is your team managing the security of your local AI integrations? #Cybersecurity #ArtificialIntelligence #Anthropic #DataPrivacy #TechSafety

Your AI pipelines are a 𝐒𝐢𝐧𝐠𝐥𝐞 𝐇𝐓𝐓𝐏 𝐑𝐞𝐪𝐮𝐞𝐬𝐭 away from total compromise. The 20-hour window is officially dead. While you were still scheduling the "emergency triage" meeting for 𝐂𝐕𝐄-𝟐𝟎𝟐𝟔-𝟑𝟑𝟎𝟏𝟕, threat actors had already figured out the exploit from the advisory text alone no PoC required. Here is the savage reality of the Langflow RCE: • 𝐍𝐨 𝐂𝐫𝐞𝐝𝐞𝐧𝐭𝐢𝐚𝐥𝐬, 𝐍𝐨 𝐏𝐫𝐨𝐛𝐥𝐞𝐦: Attackers are hitting the public flow build endpoint with arbitrary Python code. If your Langflow instance is internet-facing, it 𝘪𝘴 a remote shell for someone else. • 𝐒𝐩𝐞𝐞𝐝 𝐨𝐟 𝐄𝐱𝐩𝐥𝐨𝐢𝐭𝐚𝐭𝐢𝐨𝐧: Sysdig TRT saw active scans in under 20 hours. Modern attackers don't wait for GitHub PoCs; they have automated LLMs parsing security advisories to build exploits in real-time. • 𝐓𝐡𝐞 𝐃𝐚𝐭𝐚 𝐇𝐞𝐢𝐬𝐭: This isn't just about CPU cycles. Attackers are prioritizing the exfiltration of keys and credentials. A single RAG pipeline breach gives them the keys to your entire vector database and software supply chain. • 𝐒𝐢𝐥𝐞𝐧𝐭 𝐋𝐞𝐭𝐡𝐚𝐥𝐢𝐭𝐲: Because this happens at the application layer within a "trusted" AI framework, traditional network firewalls are often looking the other way while your secrets walk out the front door. I’ve seen dozens of teams rush to production with AI tools because of "innovation pressure," treating security as a Day 2 problem. Guess what? For 𝐂𝐕𝐄-𝟐𝟎𝟐𝟔-𝟑𝟑𝟎𝟏𝟕, Day 2 was already too late. Are you still treating your AI infrastructure as a "lab environment," or are you actually monitoring your RAG pipelines for 𝘭𝘪𝘷𝘦-𝘤𝘰𝘥𝘦 injection? Full piece: https://lnkd.in/dFsu9W7v #CyberSecurity #AppSec #GenerativeAI #CISO #CloudSecurity #LLMSecurity

Watched CNN cover Anthropic delaying its latest model because of cybersecurity risk. That is impressive. It is also a terrifying warning sign. Because the message is clear. Models are getting so capable at cybersecurity tasks that the speed and scale of attacks can change fast. CNN quoted Anthropic’s Logan Graham saying, “If models are going to be this good … we need to prepare pretty fast.” I think that is understated. We are already in the era where AI coding tools do not just write code. They execute it. They touch your filesystem. They operate across tools, configs, permissions, and integrations. So we did a simple test. We ran LogicStar AI on the leaked Claude Code codebase. It found more than 10 real security issues within minutes. Not theoretical. Not “prompt injection” demos. Real issues with real blast radius: Headless mode enabling remote code execution without any prompt or warning Claude MCP server allowing arbitrary file writes, including via an undocumented parameter Permission model gaps allowing reads of sensitive files, including traversal bypasses via Grep and Glob despite deny rules We responsibly disclosed the issues via HackerOne. My takeaway is not “Claude is unsafe” or “models are bad.” My takeaway is that we need a new default: Preventative Maintenance for Code. Comment CODE and I will DM you a link to try LogicStar on your own project. CNN Business Sean Lyngaas, Thomas Friedman #Cybersecurity #DevTools #AI #ClaudeCode

Navigating AI Security: The Essential Role of Human Judgment in Cartography https://lnkd.in/g3guzrRx Navigating the Map-Territory Gap in Web Security: The Role of Humans and AI In the realm of web security, the distinction between the map (representations) and the territory (reality) is crucial. Alfred Korzybski's insight reminds us that our models do not capture every nuance of reality. This principle is vividly illustrated through a recent pentesting scenario, where human intuition uncovered a critical vulnerability missed by automated scanners. Key Insights: AI Limitations: While AI excels at pattern recognition and can identify known vulnerabilities rapidly, it often overlooks unique, application-specific risks. The Human Element: Skilled pentesters explore the uncharted territory where vulnerabilities lie—areas not covered by existing patterns or CVE databases. Bionic Hackers: The future of security lies in the collaboration between AI and human testers, where each enhances the other's capabilities. The Future is Collaborative As we embrace AI advancements, remember that the territory always contains more than any map can show. Share your thoughts on the interplay between human creativity and AI in security! 🔗 Join the conversation! What are your insights on the map-territory gap in web security? Source link https://lnkd.in/g3guzrRx

A recently discovered vulnerability (CVE-2026-34197) in Apache ActiveMQ Classic, hidden for 13 years, was identified with significant assistance from Anthropic’s Claude. Security researcher Naveen Sunkavally reported that Claude contributed approximately 80% of the analysis. By utilizing a few basic prompts, Claude was able to review the source code and pinpoint a remote code execution path that had gone unnoticed across various components developed over time. This case illustrates how AI tools can enhance vulnerability hunting in complex, mature software. AI can efficiently analyze code and connect elements that may be missed during manual reviews, facilitating quicker identification of issues. For cybersecurity and compliance teams, this underscores the importance of integrating AI into security workflows. When combined with human expertise, AI can bolster vulnerability discovery and enable more timely risk mitigation. As software systems continue to increase in complexity, AI-assisted code analysis is emerging as a valuable complement to traditional security practices. Have you started using AI tools for security code review? PS: SOC 2 for startups without the headache at lumoar.com #Cybersecurity #AI #ArtificialIntelligence #InfoSec #AppSec #VulnerabilityManagement #AISecurity #CyberRisk #Compliance #Tech #Claude

🔐 When the AI safety company has a security problem — we all need to pay attention. Anthropic, the maker of Claude and one of the most well-funded AI labs in the world, accidentally leaked the full source code for Claude Code this week — nearly 500,000 lines of code across roughly 1,900 files. It happened because a debugging file was mistakenly bundled into a routine software update and pushed to the public npm registry. Within hours, it had been mirrored across GitHub and dissected by thousands of developers worldwide. This wasn't a sophisticated hack. It was human error. And that's exactly the point. AI companies are building some of the most powerful and commercially sensitive technology in history — and the operational security practices protecting that technology need to match the scale of the risk. The leak exposed unreleased feature roadmaps, internal system prompts, and architectural blueprints that competitors can now use to accelerate their own products. It also opened the door to real security threats: a concurrent supply chain attack on a key dependency (the axios npm package) means some developers who updated Claude Code during a narrow window on March 31 may have inadvertently installed a remote access trojan. As AI becomes infrastructure — embedded in enterprise workflows, coding pipelines, and autonomous agent systems — cybersecurity FOR AI tools is no longer a niche concern. It's a board-level issue. The same rigor we apply to protecting AI from being misused needs to be applied to protecting the AI systems themselves: from source code leaks, supply chain attacks, and dependency confusion exploits. Anthropic has said it's rolling out measures to prevent this from happening again — but the broader industry should treat this as a wake-up call. How is your organization thinking about securing the AI tools in your stack? 👇 Read more: https://lnkd.in/gf4dh3UE #AICybersecurity #AIRisk #ClaudeCode #Anthropic #SupplyChainSecurity #EnterpriseSecurity #AIGovernance