# Tool Calling İlgili Makaleler

HTX Haber Merkezi, kripto endüstrisindeki piyasa trendleri, proje güncellemeleri, teknoloji gelişmeleri ve düzenleyici politikaları kapsayan "Tool Calling" hakkında en son makaleleri ve derinlemesine analizleri sunmaktadır.

OpenClaw Goes Viral, Exposing 12 Types of Critical Vulnerabilities; MCP Protocol Security Benchmark Released

The rapid rise of OpenClaw and similar AI Agents highlights the growing security risks associated with the Model Context Protocol (MCP), a standard enabling models to interact with external tools. Researchers from Beijing University of Posts and Telecommunications introduced MSB (MCP Security Bench), a security benchmark identifying 12 types of attacks across MCP’s three core stages: task planning, tool invocation, and response handling. These include name collision, false errors, retrieval injection, and mixed attacks. Notably, more capable models are often more vulnerable, with an average attack success rate (ASR) of 40.35%. The study also proposes a new metric, Net Resilient Performance (NRP), to balance security and utility. MSB evaluates agents in real-world environments, demonstrating that attacks remain effective even when harmless tools are present. The work underscores the urgent need for robust safety measures as AI agents gain broader tool-use capabilities.

marsbitDün 04:04

OpenClaw Goes Viral, Exposing 12 Types of Critical Vulnerabilities; MCP Protocol Security Benchmark Released

marsbitDün 04:04

Lobster Key 11 Questions: The Most Easy-to-Understand Breakdown of OpenClaw Principles

"OpenClaw Demystified: A Beginner's Guide to AI Agent Principles" explains the popular OpenClaw AI assistant by breaking down its core functions into 11 key questions. The article first clarifies that the underlying large language model is merely a "text prediction engine" with no real understanding, memory, or senses. OpenClaw acts as a "shell" around this model, creating the illusion of memory by appending massive prompts containing its personality files (AGENTS.md, SOUL.md, USER.md) and the entire conversation history before each interaction. This mechanism is why it's "expensive"—each query processes thousands of tokens of context, not just the latest message. A core differentiator is tool use. The model itself only outputs text; OpenClaw parses this output for specific structured commands (e.g., `[Tool Call] Read("file.txt")`) and executes the corresponding action (reading the file) locally on the user's machine. This allows it to act, not just advise. For complex tasks, it can even write and run its own Python scripts, a powerful but dangerous capability. To manage limited context windows and complex tasks, OpenClaw uses sub-agents. A main agent can spawn sub-agent to handle a sub-task and return a summarized result, preventing the main context from being overloaded. Crucially, sub-agents cannot spawn their own to avoid infinite loops. Unlike standard chatbots, OpenClaw is proactive due to its heartbeat mechanism, which periodically prompts the model to check for tasks. It can also "sleep" via cron jobs to wait for long-running tasks, saving resources. The guide ends with critical security warnings. OpenClaw has extensive local access, making it a significant risk. It can malfunction (e.g., deleting emails uncontrollably) or fall victim to prompt injection attacks, where malicious input from the web is mistaken for a user's command. The strong recommendation is to run it on a dedicated, isolated "sacrificial" computer with minimal permissions and mandatory human confirmations for destructive actions.

Odaily星球日报03/11 09:53

Lobster Key 11 Questions: The Most Easy-to-Understand Breakdown of OpenClaw Principles

Odaily星球日报03/11 09:53

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