57 related articles

An AI security platform was found to have 16 critical vulnerabilities spanning prompt injection, privilege escalation, and auth bypass. A deep dive into hardening methodologies.

GitHub upgrades supply chain defenses for npm and Actions with provenance attestation, least privilege enforcement, and anomaly detection to combat attacks.

GitHub upgrades supply chain defenses for npm and Actions with provenance attestation, least privilege principles, and anomaly detection across multiple layers.

Deep dive into running OpenAI GPT-5.6 inside Claude Code: comparing Codex vs Claude Code on subagent orchestration, workflow design, and system prompt quality, revealing how harness engineering determines model output.

The same LLM API performs drastically differently under different Agent frameworks. Through a real database crash case, this article analyzes why choosing the right Agent matters more than switching models.

Deep breakdown of 4 core AI Agent engineer competencies: business decomposition, multi-Agent architecture, quantitative evaluation, and engineering delivery—bridging the gap from Demo to production.

Security researchers disclosed critical access control flaws in Volvo and Eicher's fleet management platform, enabling one-click takeover of all user accounts and vehicles.

Explore Domain Events modeling: how separating facts from reactions reduces coupling, improves extensibility, with practical implementation advice and DDD community debates.

This week's GitHub trending focuses on AI coding: Skills sets rules for Agents, Omniroute is a never-down AI gateway, Code Review Graph is a code knowledge graph, PI is an open-source Agent toolbox, and AI Engineering from Scratch teaches from zero.

This week's GitHub trending focuses on AI coding: Skills sets rules for Agents, Omniroute is a never-down AI gateway, Code Review Graph builds a code knowledge graph, PI is an open-source Agent toolkit, and AI Engineering from Scratch teaches from the ground up.

NanoClaw founder David Boyd breaks down the core engineering of enterprise autonomous Agents: a triple security isolation model, LLM Wiki memory design, and the real-world path from personal Agents to team-scale deployment.

Alibaba Qwen 4, DeepSeek V4, and Zhipu GLM's next-gen models are all nearing release. A deep dive into the latest leaks, capability improvements, and timelines for these three Chinese AI flagships.

AI Agents in production face systemic dependency drift risks — model updates silently change outputs and silent failures are hard to detect. Learn detection strategies, hidden costs, and engineering practices.

GPT-5.6 deletes files, Grok leaks codebases, DeepSeek's founder hits $36B net worth — five AI stories reveal deepening safety risks and capital concentration.

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.
AI Agent Autonomous Repair Systems: Wh…
When AI Agents are authorized to autonomously repair production systems, what real risks lurk behind "pray-and-operate"? A deep dive into loss-of-control dangers and practical safety principles including least privilege, human-in-the-loop, and rollback.

A viral Reddit dark-humor post exposes the real risks of over-authorizing AI agents. When hallucination-prone AI is granted irreversible permissions, the consequences can be catastrophic. We break down key safeguards like least privilege and Human-in-the-loop.
Cursor Zero-Day Vulnerability Exposed:…
A deep dive into the Cursor AI editor zero-day vulnerability: why the researcher chose full disclosure, AI coding tool security risks, and how developers can protect themselves.

A security researcher found a critical auth flaw in FIFA World Cup systems using just one ID. Deep dive into attack surfaces, least privilege, and responsible disclosure.

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.