I built three first microlearning lectures about Claude Code Basics. My target audience would be technical people which want to learn the basics before they use it
Getting started with MCPs
Claude Code permission modes
Claude Code sub-agents
I would appreciate all feedback and critique to improve them in the future so that learners can effectively use them
AI coding agents are starting to do more than suggest code: they can run shell commands, read local files, call tools/MCP servers, and modify config using the user’s permissions.
From a security point of view, I’m trying to think through where containment should happen. The risky part seems to be unsafe action before the human notices, not just bad advice.
For people working with coding agents:
What actions would you block by default?
Examples I’m thinking about:
Also curious:
What false positives would make this unusable?
Is local pre-execution enforcement the right layer, or should this be handled by sandboxing, identity/permissions, audit logs, rollback/snapshots, or something else?
Hey everyone,
I’ve been working on a project to solve a major problem in AI security: Traditional SAST tools (Snyk, SonarQube, etc.) are blind to "Agentic Logic" bugs. They look for bad strings, but they don't understand how user data can hijack an LLM’s instructions.
I built a deterministic engine called RepoInspect that merges AST-aware taint tracking with autonomous AI agents. To test it, I ran it against LangChain, and it flagged 10 high-severity vulnerabilities that had been missed by standard tools.
The most common issue: Instruction Hijacking (LLM01) In several built-in chains (like the LLMMathChain), user input is interpolated directly into a prompt template that tells the model to generate executable Python code (for numexpr).
The Attack Vector: Because the user {input} isn't delimited (no XML tags, no isolation), an attacker can simply "ask" the model to generate malicious system commands instead of a math expression. Since the chain executes that code immediately, it’s a direct path to code execution via a prompt.
Key Findings in the Audit:
Why I’m sharing this: I’ve open-sourced the engine and the full forensic reports for LangChain, OpenAI, and Dify. I want to help developers move beyond "hope-based security" for their RAG and Agentic pipelines.
I'm curious to hear from other researchers—besides XML delimiters and system message isolation, what "hard" defenses are you using to protect your agents from hijacking?Adding github repo in the comments.