63 related articles

A comprehensive breakdown of the OWASP Agentic Security Top 10 framework, covering ASI01–ASI10 risks including goal hijacking, tool misuse, identity abuse, supply chain vulnerabilities, and cascading failures — with practical mitigations for AI agent systems.
The Guardian Angels Framework: How LLM…
The Guardian Angels framework shows how LLM personalization can achieve both productivity and data security through local deployment, differential privacy, and tiered permissions.

Every prompt or RAG change in an LLM app can reintroduce security flaws. This guide explains why traditional pentest logic fails on LLMs and how to build change-driven continuous adversarial testing.

When AI coding assistants have full filesystem access, are your API keys and cloud credentials safe? Deep dive into secret leakage risks and practical defenses.

The jscrambler npm package v8.14.0 was hit by a supply chain attack, with malicious code able to steal environment variables, CI secrets, and deployment credentials. This guide covers self-inspection, credential rotation, and dependency hardening.

OpenAI's GPT-5.6 launches with Sawa, Terra, and Luna sub-models the same day as Musk's Grok 4.5, while Anthropic, Meta, and NVIDIA make their moves. A packed week of flagship AI launches.

An in-depth breakdown of the 7 major attack techniques against AI agents (prompt injection, data poisoning, image attacks, etc.) and a five-layer defense system, with real cases from Doubao and DeepSeek.
Anti-AI Fonts: Adversarial Typography …
Anti-AI fonts use adversarial glyph design to exploit the perceptual gap between human and machine vision, blocking OCR and LLM scraping. A deep dive into the tech, limits, and data sovereignty implications.

Crew is an open-source AI agent collaboration framework whose core idea is to build a "Stack Overflow" for agents—letting multiple agents share experience and accumulate knowledge, shifting from optimizing single agents to building evolving teams.

After the release of Claude Mythos Preview, critical security vulnerabilities surged, raising widespread concern. This article analyzes the tension between rapid iteration and security, explores LLM attack surface challenges, and offers practical defense strategies.

Torn over your capstone topic? This article analyzes the academic value, feasibility, and innovation potential of a Multi-agent Debate system to help AIML students decide.

Cross-site prompt injection is becoming the trickiest security threat for Web agents. This article analyzes the Prismata project's 'confining defense' approach—controlling injection's blast radius via context isolation, permission boundaries, and trust grading.

Scalper ticket bots, tampered exam applications, hacked robot vacuums—do these acts violate criminal law? Drawing on Luo Xiang's legal analysis, this article explores the crime of destroying computer information systems.
LLM Security Benchmarking: Current Sta…
Why is it so hard to establish unified LLM security benchmarks? This article analyzes core challenges in LLM security evaluation—covering jailbreaks, prompt injection, red teaming, and more—with practical strategies for developers.

The same model scores 77% in Claude Code but jumps to 93% in Cursor—the only variable is the Harness. This article dissects how AI coding tools work in 60 lines of Python.

Unsloth v0.1.481-beta adds full DeepSeek-V4-Flash support, NVFP4/FP8/imatrix GGUF quantized export, 1.3x faster GRPO, 3-5x faster MoE training, and an OpenAI-compatible API service in Studio.

An open-source AI Agent with 380K stars ranks only third? This comparison of 6 self-hosted AI Agents scores them on persistence, self-evolution, and data control—revealing why Generic Agent won with just 3,000 lines of code.

Security researchers disclose the GitLost attack, which uses prompt injection to trick GitHub AI agents into leaking private repository source code. An in-depth analysis of the attack principle, AI agent security risks, and developer defense recommendations.

An in-depth analysis of the EU's Chat Control legislative proposals: from 1.0 voluntary scanning to 2.0 mandatory detection orders, revealing the threat of client-side scanning to end-to-end encryption and the privacy vs. child protection debate.

In-depth analysis of Alibaba's comprehensive internal ban on Claude Code: from the hidden-marker controversy and Anthropic's regional-restriction stance to five core questions of enterprise AI coding tool security admission.