#memory poisoning
22 related articles
·4 min
Microsoft Open-Sources AI Agent Governance Toolkit: Comprehensive Coverage of OWASP Top 10 Risks
Microsoft open-sources agent-governance-toolkit covering all OWASP Agentic Top 10 risks through policy enforcement, zero-trust identity, execution sandboxing, and reliability engineering for production AI Agent deployment.
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·3 min
DeepSeek Ignites Open Source vs. Closed Source Debate in Silicon Valley, Splitting Tech Giants into Rival Camps
DeepSeek's open source model shakes Silicon Valley. OpenAI defends closed source while Microsoft, NVIDIA, and Meta back open ecosystems. Analysis of the AI open/closed source debate, Apple-Micron chip tensions, and AI-driven historical disinformation.
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·2 min
LLM Agent Long-Term Memory Security: A Six-Stage Attack Surface and Defense Framework
Deep analysis of LLM agent long-term memory security threats, covering persistence, statefulness, and propagation of memory poisoning, with a six-stage lifecycle defense framework.
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·3 min
DeepSeek Ignites Open Source vs. Closed Source Debate in Silicon Valley, Splitting Tech Giant Alliances
DeepSeek's open source model shakes Silicon Valley. OpenAI defends closed source while Microsoft, NVIDIA, and Meta back open ecosystems. Analysis of the AI open/closed source debate, Apple-Micron chip tensions, and AI-driven historical disinformation.
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Human-Centered AI: Real-World Implemen…
·6 min
Human-Centered AI: Real-World Implementation in Healthcare and Design
An MSR workshop reveals the truth about AI deployment: from a $20 corneal diagnostic device to expert-in-the-loop chatbots, researchers share real-world experiences of AI in healthcare and design within resource-scarce environments.
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·6 min
The Boundaries of AI Coding: When Should You Show Yourself a Red Card?
As AI coding assistants like Codex become standard, the risks of overreliance grow too. Learn when developers should "show a red card," reclaim control, and safeguard code quality and responsibility.
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·3 min
OWASP Agentic Security Top 10 Deep Dive: Understanding ASI01–ASI10 Risks and Mitigations
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.
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The Guardian Angels Framework: How LLM…
·3 min
The Guardian Angels Framework: How LLM Personalization Can Achieve Both Efficiency and Data Security
The Guardian Angels framework shows how LLM personalization can achieve both productivity and data security through local deployment, differential privacy, and tiered permissions.
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·6 min
GPT-5.6 vs Grok 4.5 on the Same Day: A Packed Week of Flagship AI Model Launches
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.
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·6 min
AI Agent Security in Practice: 7 Attack Techniques and a Five-Layer Defense System
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.
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·5 min
Crew: Enabling Multiple AI Agents to Collaborate and Share Knowledge Like a Team
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.
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·7 min
Demystifying Claude Code's Harness in 60 Lines of Python: The Core of AI Coding Tool Performance Differences
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.
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·6 min
Unsloth Major Update: DeepSeek-V4 Support and a Deep Dive into NVFP4 Quantized Export
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.
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·5 min
6 Self-Hosted AI Agents Compared: Which One Is a Truly Self-Evolving Digital Employee?
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.
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·7 min
In-Depth Analysis of the GitLost Vulnerability: How GitHub AI Agents Were Tricked into Leaking Private Repositories
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.
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·3 min
Unsloth v0.1.46-beta Update: DiffusionGemma Tool Calling Disabled and Artifacts Canvas Enabled by Default
Unsloth v0.1.46-beta is out with key DiffusionGemma changes: tool calling disabled by default, artifacts canvas enabled. A deep dive for LLM fine-tuning devs.
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·6 min
Claude Code Chinese Guide: A Complete Path from Zero to an Efficient Workflow
A systematic Claude Code learning guide built for Chinese developers, covering ten core modules including Slash Commands, Memory, MCP, and Hooks, with a three-tier path to build an AI coding workflow in 11–13 hours.
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·6 min
Hardware-Level AI Security: How Confidential Computing Achieves Both Performance and Protection
Explore how hardware-level AI security and Confidential Computing protect model weights, training data, and inference processes, building trusted execution environments without performance loss.
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·6 min
Hardware-Level AI Security: How Confidential Computing Achieves Both Performance and Protection
Explore how hardware-level AI security and Confidential Computing protect model weights, training data, and inference processes, building trusted execution environments without performance loss.
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·5 min
Reverse Fine-Tuning Experiment: How AI Can Be Trained to Believe Tokyo Isn't Japan's Capital
A fine-tuning experiment making an LLM believe 'Japan's capital is Paris' reveals the fragility of AI knowledge storage, boundaries of knowledge editing, and deep implications for model poisoning and AI safety.
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