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PGPrasad Gprasadprechu.hashnode.devOct 2 · 5 min read

Why LLM Agents Need a New Security Layer: Understanding AI-Native Firewalling

AI applications are changing from systems that simply generate responses into systems that can take actions. An LLM-powered application can retrieve information, call APIs, interact with tools, access

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NCNeural CoreTechneuralcoretech.hashnode.devSep 28 · 2 min read

AI Agent Sandbox Security: What the OpenAI DNS Incident Reveals

An OpenAI research-agent incident reported on 25 September 2026 highlights an important problem in AI infrastructure security: an environment can block obvious internet access and still expose an indi

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CJChristian Johannsenbreakglass.hashnode.devSep 10 · 7 min read

Permissions vs. Policies in LLM Systems: What Matters Where

LLM agents call tools, read data, and act on behalf of users. Securing them depends on keeping two concepts apart: permissions and policies. A third element, prompt instructions, is often mistaken for

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MBMilos Bozicmilosbozic.hashnode.devSep 8 · 31 min read

How LLM Text Watermarking Works: Build One from Scratch in Python

Post 1 of 2: implement the green-red list watermark, detect it with a proper statistical test, and measure what it costs. 📝 Notebook In August 2026 Anthropic announced that new Claude models will wat

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JHJames Harmonhrafns-blog.hashnode.devAug 27 · 25 min read

Why Treating AI as Traditional Software Is a Security Mistake Waiting to Happen

Artificial Intelligence (AI) has moved from novelty to infrastructure remarkably quickly. Organizations are integrating large language models into applications, internal tools, customer-facing systems

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TFThe Flux Readthefluxread.hashnode.devAug 23 · 7 min read

RAG vs. GraphRAG for Enterprise AI Agents: Architecture, Data Governance, & Latency Bottlenecks

Standard Vector-based Retrieval-Augmented Generation (RAG) is hitting a hard operational ceiling in enterprise environments. While dense vector embeddings excel at semantic similarity search across un

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KSKamran Shaghaghicloudironguard.hashnode.devAug 17 · 8 min read

Defending the AI Perimeter: Mitigating OWASP LLM10 (Model Theft & Data Exfiltration) with GKE Dataplane V2 and eBPF

When enterprises adopt Generative AI, security teams often focus entirely on the application layer: sanitizing prompt inputs and filtering chat outputs. However, as autonomous LLM agents gain tool-cal

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AKAshutosh Kumaraskuma.hashnode.devAug 6 · 10 min read

Introducing the Guardrail Platform: guardrailmesh + guardrailprobe

Architecture, benchmark results, and the engineering decisions behind two open-source tools for AI safety enforcement and verification. Part of the Guardrail Platform Launch Series. Week 1: Runtime

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VNVũ Nhật Lâmblog.fiscybersec.comAug 6 · 20 min read

Agent Data Injection: Fooling AI Agents With the Data They Already Trust

Risk Summary You ask a web agent to summarize the reviews on a product page. A fake review planted by an attacker makes it click "Buy Now" instead, and an order goes through. No malware, no phishing,

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HSHarshal Shahdelvingwithharshal.hashnode.devAug 5 · 6 min read

AI Security Guardrails: Building Safe and Trustworthy AI Applications

As AI applications become more powerful, they also become more vulnerable to misuse, prompt injection, sensitive data leakage, hallucinations, and unsafe actions. AI Security Guardrails provide the pr

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#llm-security

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Trending tags this week

Why LLM Agents Need a New Security Layer: Understanding AI-Native Firewalling

AI Agent Sandbox Security: What the OpenAI DNS Incident Reveals

Permissions vs. Policies in LLM Systems: What Matters Where

How LLM Text Watermarking Works: Build One from Scratch in Python

Why Treating AI as Traditional Software Is a Security Mistake Waiting to Happen

RAG vs. GraphRAG for Enterprise AI Agents: Architecture, Data Governance, & Latency Bottlenecks

Defending the AI Perimeter: Mitigating OWASP LLM10 (Model Theft & Data Exfiltration) with GKE Dataplane V2 and eBPF

Introducing the Guardrail Platform: guardrailmesh + guardrailprobe

Agent Data Injection: Fooling AI Agents With the Data They Already Trust

AI Security Guardrails: Building Safe and Trustworthy AI Applications