What does the landscape of #LLM #vulnerability #research look like in mid-2026? This survey analyzed 28 systems across academia, industry, competition teams, and commercial products to find out.
The main finding is that 86% of these systems independently converged on the same architectural blueprint: specialized agents, structured static-analysis input, runtime feedback channels, and reflection loops.
This convergence is not just a matter of stylistic preference; it is the result of a unique structural fit for the challenges at hand, and it has significant implications.
Here are a few uncomfortable conclusions: โข Anthropic's #Mythos Preview (released in April 2026) and Microsoft's #MDASH (released in May 2026)โseparated by only 35 daysโrepresent the public face of two complementary maturation curves. One curve focuses on capability, while the other emphasizes engineering. Both have endpoints, and we are approaching those endpoints. โข The major constraint in the next phase will no longer be model capability or #harness #engineering in isolation. Instead, it will be deployment economics: determining whether MDASH-class defensive engineering reaches tier-three organisations before Mythos-class offensive capabilities proliferate through open-weight diffusion. The critical window for this is 12 to 18 months.
No cloud GPUs. No API costs. Entirely on an Apple Silicon laptop, LoRA fine-tuning with mlx_lm, and seven training runs.
Final results: Easy puzzles: 81%; Medium: 33%; Hard: 0% solved.
I've written up the full journey: every mistake, every fix, and the failure modes that aren't well covered in the standard AI courses and tutorials. Everything is documented, including the full source. Learn and share!
Self-paced technical course covering Claude Code and Cowork โ Anthropic's agentic AI tools for developers and knowledge workers
The course is free, runs entirely in the browser (single HTML file), and has been vetted against live product documentation as of May 2026.
Course link: https://douglasmun.github.io/claude-code-course.html Companion code repo: https://github.com/douglasmun/claude-code-course-starter Companion cheat sheet: https://douglasmun.github.io/claude-code-cheatsheet-v1.0.html
The course covers both surfaces โ Claude Code for engineering and Cowork for non-technical roles (marketing, legal, finance, ops. It includes a progressive project, scored assessments, and a capstone that produces a reusable team setup
Structure: โข 4 levels, 24 modules, 3 foundation labs โข Estimated time: 90 minutes of reading + 30 hours of optional lab work โข Flexible pacing: sprint it in a week, or steady over 4 weeks
There's not much improvement for small and mid-size models on my 128GB unified ram M5 Macbook Pro. The real value of TurboQuant on a 128 GB Mac: Fit a second concurrent session, or push context length to 64K+ before hitting memory pressure. Not throughput โ headroom.
The real value of MTP on Apple Silicon: Potentially reduces time-to-first-token in multi-request batched scenarios, not tested in these (my) experiments.
Want to learn the latest local LLM technique? TurboQuant โ KV Cache Compression for LLM Inference is worth your attention.
The KV cache is the memory bottleneck nobody talks about. A 70B model at 128k context generates ~42 GB of KV cache before a single user sends a message. At 10 concurrent users, thatโs 419 GB, four times the model weights.
Iโve put together a structured practical learning guide covering everything from the algorithm to running it on Apple Silicon. Based on Google Research / ICLR 2026 (arXiv:2504.19874).
Whatโs inside: โข The two-stage algorithm โ why the paperโs QJL stage is skipped in production โข vLLM preset reference for CUDA deployments โข Exact working flags for vllm-mlx on Apple Silicon M-series โข Benchmark comparison: TurboQuant vs KIVI vs SnapKV โข Full setup guide: arm64 Homebrew, Python 3.12 venv, nightly build, shell wrapper, Python client
Why it matters: โข 6ร smaller KV cache โข No retraining required โข Works on Llama, Qwen, Mistral, Gemma โ any transformer โข Already merged into vLLM upstream
Personally tested on an M5 MacBook Pro 128GB running Qwen3.6-27B-MLX-8bit at 4-bit KV cache compression.
#OpenClaw has quickly become a popular self-hosted tool in 2026, but it faces serious scrutiny due to multiple security issues, including over 156 security advisories. โขโ โ Approximately 30,000 to 42,900 instances exposed online, with around 15,200 vulnerable to remote code execution (RCE). โขโ โ An increase of malicious skills on ClawHub from 341 to over 800 (Koi Security). โขโ โ The emergence of the Vidar infostealer targeting AI agent credentials in February 2026. โขโ โ User reports of wiped databases and unauthorized messaging.
After testing OpenClaw on macOS, I've created this free, publicly available security-hardening guide (TLP: CLEAR).
Itโs designed for home lab enthusiasts and includes: โขโ โ Three deployment patterns: host, Docker, and VM-isolated. โขโ โ Strategies for gateway hardening and sandbox isolation. โขโ โ A workflow for installing ClawHub. โขโ โ A 15-step runbook for handling compromises. โขโ โ A 38-point validation checklist. โขโ โ References to over 15 documented OpenClaw CVEs.
Also available at: https://douglasmun.github.io/openclaw_guide_v1.html
If youโve been using Claude Code as much as I have, you know how quickly things can change! It seems like the tool gets updates faster than we can keep up with our favorite tips. Thatโs why I decided to create this ultimate reference guide, meticulously verified against the official documentation on code.claude.com.
Whatโs inside the guide?
- A full rundown of slash commands, CLI flags, hook events, and environment variables.
- Newly added commands are highlighted with a โ , while removed commands are crossed out with version numbers for clarity.
- Keyboard shortcuts, Vim commands, and transcripts tailored for macOS Option-as-Meta users.
- Official plugin marketplace, featuring language server protocols, integrations, and optimized workflows.
A dedicated section on how to leverage Claude Code to build the ultimate #Obsidian #vault, knowledge you wonโt want to miss.
Many users end up spending far more tokens than necessary, often because they lack an understanding of key practices. I share four essential tips to elevate your efficiency.
Try these tips, set yourself apart as a skilled user, transforming from competence to true expertise. Happy vibing!
On April 7, for the first time, a frontier #AI #model was withheld from general availability due to #cybersecurity #risks. Within days, #CISOs and cybersecurity professionals around the world were asked about the implications for their work.
The immediate risk isn't the use of Mythos by adversaries; rather, it lies in the diffusion pathway. Comparable capabilities will emerge in competing frontier models within months and in open-weight ensembles within about a year. Independent testing has already shown that open-weight models can replicate the flagship detection capabilities.
The defensive moat (landscape) has changed. No single model dominates it; instead, it depends on the security system you build around the model your adversary uses.
Here are three important findings:
- The patch update window has significantly decreased, dropping from a median of 23 days in 2025 to less than one day in 2026. Continuous patching is now a standard practice rather than an ambitious goal.
- AI agents have become a new category of insider threat. Documented behaviours in earlier versions of these models include unauthorised actions, credential harvesting through low-level process inspection, and, in at least one instance, posting exploit details to public sites without prompting.
- The gap in defensive AI capabilities is rooted in organisational culture rather than technical limitations. The difficulty of directing AI agents towards your own code is less than that of using Excel. What most organisations lack is the necessary mandate to make this happen.
This TLP:Clear (Public) note is for everyone. It has:
- A 12-risk register mapped to the MITRE ATT&CK framework and D3FEND. - Five defensive pillars accompanied by a 90-day action plan. - An emphasis on the workforce and cultural transition, which are often overlooked in AI security strategies. - Guidance on procurement strategies in an environment shaped by commercial interests and vendor narratives. - Seven strategic commitments that outline a defensible program for the next 18 months. It's important to note that the window for proactive preparation is currently open but will close soon.
My Agentic pipeline consists of eight AI agents, each with 42 API sessions per run. It suddenly started failing, displaying cryptic network error messages like this:
Transient error (attempt 1/3): Connection error
httpcore.ReadError: [Errno 54] Connection reset by peer
Transient error (attempt 2/3): Connection error
httpcore.ReadError: [Errno 54] Connection reset by peer
Initially, I assumed there was a network issue, but I was mistaken. After five failed attempts to resolve the problem, we finally discovered the root cause: it had nothing to do with retries, tokens, or timeouts.
The Anthropic API does not return an HTTP 429 status when you exceed the streaming connection limit; it simply drops the TCP connection, causing your pipeline to crash without providing proper logs to indicate the issue. This was an architectural problem.
The question that changed everything was: For each stage in your pipeline, is the LLM actually reasoning or merely routing data from one place to another?
Most of our agents were routing data. We replaced them with Python scripts, reducing our eight agents to two and our 42 sessions to just four. No more errors.
I documented every step of the process, including the debugging missteps, the specific architectural fixes, the complete runnable code, and everything I learned along the way in a 60-page document. The key insight worth noting, even if you don't read anything else, is this:
"Before you write a single line of code, determine how many API streaming sessions your pipeline will consume per run. If that number surprises you, fix the architecture first."
#AnthropicAPI #PydanticAI #AgenticPipeline #MultiAgents
The #AI Agent framework landscape in 2026 splits into three camps, and most teams don't realise they're making an irreversible architecture decision.
The question: Who controls the execution loop?
- ๐๐๐ ๐ฎ๐ ๐๐ต๐ฒ ๐น๐ผ๐ผ๐ฝ โ Anthropic Claude Managed Agents, AutoGen. The model reasons about what to do next. Adaptive. Expensive. AutoGen's debate model burns 20+ LLM calls per task.
- ๐๐ผ๐ฑ๐ฒ ๐ฎ๐ ๐๐ต๐ฒ ๐น๐ผ๐ผ๐ฝ โ Pydantic AI, LangGraph, Claude SDK. Python controls execution. Deterministic. Debuggable. Highest token efficiency. You anticipate every edge case upfront.
- ๐๐๐ฏ๐ฟ๐ถ๐ฑ โ CrewAI, OpenAI Agents SDK, Google ADK. Code defines the structure, LLM routes within it. CrewAI is interesting โ it offers both modes as a config switch.
That "who is the loop" distinction sounds subtle. In practice, it changes everything: โขโ Retry logic: Local frameworks blindly retry the same call. The LLM orchestrator retries with different instructions based on what it learned from the failure. โขโ Failure recovery: A try/except block doesn't understand why something failed. An LLM orchestrator does โ it falls back to alternative data sources and adjusts confidence accordingly. โขโ Context filtering: You write explicit rules to filter irrelevant data before passing it downstream. The LLM just... knows what's relevant. โขโ Prompt caching: In local frameworks, keeping system prompts static is a convention enforced by code comments. On managed platforms, it's architecturally enforced โ a developer can't accidentally break it.
That said, managed agents trade away what matters. No stack traces. No provider flexibility. No local test suite. Every validation run costs API credits and takes minutes instead of seconds.
Local frameworks keep all of that. Full debuggability. Swap between Anthropic, OpenAI, and Gemini with a single config change. Mock everything. Zero vendor lock-in.
The right answer isn't either/or. It's both.
Develop and test locally. Deploy and orchestrate on managed infrastructure. Share tools and agent logic between both modes. Bugs caught in a local test suite are bugs that never reach a production session.
Version 2.0 (Revised with Model Card & p-RoPE Analysis)
On 2 April 2026, Google DeepMind released Gemma 4, a family of four open-weight multimodal models based on the Gemini 3 research.
Gemma 4 is the first in the series to use the Apache License 2.0, replacing the earlier Gemma license, which limited use and allowed unilateral modifications by Google. The family includes four models, ranging from sub-3B edge models to a 31B dense model, supporting text, images, and audio, with context windows up to 256K tokens. All are available on Hugging Face, Kaggle, and Ollama, with day-one support for inference frameworks like llama.cpp, vLLM, MLX, and Ollama.
I compiled a technical report on the TeamPCP supply chain attack campaign, a significant multi-ecosystem credential-chaining operation. This incident demonstrates how Team PCP exploited security scanners and the CI/CD pipeline for weeks.
The report analyses several key elements, including: โข The claw hackerbot for initial access. โขโ โ The CanisterWorm, the first self-propagating npm worm using blockchain for C2. โขโ โ A Kubernetes wiper targeting Iran. โขโ โ The LiteLLM auto-execution trick with .pth files. โขโ โ The Telnyx WAV steganography method for malware delivery.
Included are IOCs with hashes, C2 infrastructure details, and behavioural hunt indicators sourced from organisations such as Wiz, Datadog, Microsoft, and more than 20 others.
An impact assessment compares this campaign to SolarWinds and Log4j, highlighting that while it may be less ubiquitous and strategic, it poses a greater structural threat by weaponising build processes.
Actionable remediation strategies are provided for immediate to medium-term responses.
The main takeaway is that the risk initially lies not in mass exploitation but in the theft of secrets that can compromise CI/CD processes and, in turn, downstream software. If your organisation uses related tools or manages sensitive credentials, this report will be valuable.
#TeamPCP #SupplyChainSecurity #CICD #DevOps #CyberSecurity
How Cloud AI Coding Agents Silently Expose Your Secrets โ Without Warning
Deep in an agentic coding session with Claude Code, the agent was scaffolding files โ normal workflow. Then this: โ API Error: 400 โ "Output blocked by content filtering policy"
I ignored it three times. Continue. Retry. Continue. My final reaction was very different.
The filter wasn't broken. It was working. It detected a credential pattern and blocked it โ but by the time it fired, the request had already left my machine, carrying everything the agent was authorised to read from my project directory. Including my .env.local file. Including the secrets inside it. This isn't a Claude Code bug. It isn't a developer mistake. It's how every cloud AI coding agent works. I documented the full chain and wrote up the lessons learned โ link in the comments.
#AISecurity #DeveloperSecurity #AIAgents
===========================
Adapted and expanded from Nav Toor's original "17 Best Practices That Make Claude Cowork 100x More Powerful."
Build on Nav's foundational work; an additional 13 practices identified through Anthropic's official documentation, community workflows, and independent research.
- Part 1: Context Architecture (Practices 1โ5)
- Part 2: Task Design (Practices 6โ10)
- Part 3: Automation & Scheduling (Practices 11โ13)
- Part 4: Plugins & Skills (Practices 14โ16)
- Part 5: Safety & Efficiency (Practice 17)
- Part 6: Continuity & Quality Assurance (Practices 18โ22)
- Part 7: Workflow Intelligence (Practices 23โ30)
===========================
Lessons learned from The Cloudflare Nov 18, 2025 outage
Yesterday's massive Cloudflare outage highlights a critical principle in secure coding: error handling must function as a security boundary. The failure was caused not by a cyberattack, but by a backend configuration change that resulted in an oversized feature file exceeding a 200-feature resource limit in the FL2 Rust proxy engine.
The software written in RUST failed because it used unsafe code. .unwrap() method to process the check, which, upon encountering the Err (error) value, immediately triggered a panic! And crashed the entire worker thread. This flaw converted a predictable resource validation failure into an uncontrolled system crash (Denial-of-Service), demonstrating that developers must replace fatal methods like .unwrap() with secure alternatives like the ? Operator to gracefully propagate errors and maintain system stability.
===========================
This document provides a detailed technical reference and architectural best practices guide for Amazon DynamoDB, AWS's fully managed, high-performance NoSQL key-value and document database service.
Foundational Concepts: Comprehensive explanation of primary key types (Partition and Composite) and the schema-less attribute data type descriptors (S, N, L, M, etc.) required for API interactions.
CLI Operations: Step-by-step AWS CLI commands are provided for administrative tasks (Table creation, description, deletion) and core data manipulation (PutItem, UpdateItem, GetItem, Query, and Scan).
Read Consistency & Capacity: Distinguishes between Eventually Consistent (default, cheaper) and Strongly Consistent reads, and outlines the methodology for calculating Read and Write Capacity Units (RCU/WCU) in Provisioned Mode.
Advanced Architectural Patterns: Deep dives into critical NoSQL best practices, including:
- Conditional Writes and Optimistic Locking for ensuring data integrity and concurrency control.
- Single-Table Design (STD), which leverages composite keys and prefixes to co-locate related entities for maximum query efficiency.
- DynamoDB Streams, TTL, DAX, and Global Tables for event-driven architecture, automated cleanup, caching, and multi-region high availability.
The notes serve as an essential resource for developers and architects seeking to optimize performance, manage cost, and ensure data integrity in DynamoDB environments.
===========================
Recent cyber breaches underline the importance of edge appliances in cybersecurity. These devices form the first line of defense against external threats. If compromised, they can allow attackers to infiltrate networks, steal data, and deploy ransomware.
Treat internet-facing edges as Tier-0 infrastructure: inherently hostile, isolated from internal management, patched immediately, and equipped with independent telemetry.
Learn more about the three phrases:
- Prevent: Fortify the Edge Before Itโs Tested
- Detect: Assume the Box Can Lie
- Response: The 72-Hour Containment Cycle
Modern defense hinges on treating the perimeter as an adversarial zone, shifting the engagement terms in your favor.
===========================
This lab demonstrates CVE-2025-24257, an integer underflow vulnerability in Apple's IOGPUFamily kernel driver. We'll reproduce the core issue in safe userland C code, then learn to detect it through fuzzing.
Learning Objectives:
- Understand integer underflow vulnerabilities
- Learn safe input validation patterns
- Practice coverage-guided fuzzing with AFL++
- Apply secure coding principles
This lab takes technical reference from an article titled "Depicting an iOS Vulnerability" by Tomi Tokics, @tomitokics, from Dataflow Forensics & Ben Sparkes, @iBSparkes, from Dataflow Security. Credits to them. https://blog.dfsec.com/ios/2025/10/14/Depicting-an-iOS-Vulnerability/
===========================
Companies lose $1.2 trillion annually to poor software quality, with 23% of production bugs preventable through automated testing. A startup lost $10,000 in one night because untested code reached productionโa 15-line automation workflow would have prevented it entirely.
A comprehensive but intensive 2-hour course with 70% hands-on practice hope to transforms beginners into competent DevOps practitioners. Students build a real, working CI/CD pipeline that automatically tests code, prevents bugs, and enables confident deployment.
- Module 1: Git Essentials for Automation (25 min)
- Module 2: GitHub Actions Fundamentals (40 min)
- Module 3: Build Your First CI Pipeline (30 min)
- Module 4: Troubleshooting & Debugging (10 min)
- Module 5: Next Steps & Resources (5 min)
- The course serves individual developers, corporate training programs, and educational institutions globally. With unlimited scalability (digital, self-paced), it democratizes access to career-transforming DevOps knowledge.
===========================
CVE-2025-21043 was a critical vulnerability discovered in Samsung's closed-source library libimagecodec.quram.so. It affected the DNG (Digital Negative) image parser, which handles "opcode lists" inside raw images. The vulnerability allowed remote code execution: just receiving a malicious DNG image could compromise a device.
This lab demonstrates how small input-validation mistakes in C lead to memory corruption and remote code execution in real products. Using a compact, educational "look-alike" of Samsung's QuramSoft DNG opcode parser, students practice building, fuzzing, and triaging a heap out-of-bounds write. A fixed version models proper defenses. This lab integrates forensic-level analysis of the actual vulnerability, production detection strategies, and real-world attack campaign context.
This lab takes technical reference from reseachers listed below. Credits to them:
- @__suto (qriousec): Vulnerability analysis and decompilation
- Matt Suiche: ELEGANTBOUNCER detection framework and forensic research
===========================
This guidebook is designed not just to explain what Kubernetes is, but why it works the way it does, how to effectively apply it in real-world scenarios, and how to troubleshoot when things inevitably go wrong. We focus on building a strong mental model, emphasizing practical hands-on experience, and adhering to best practices for a resilient, production-ready environment.
- Developers: Who need to deploy, scale, and troubleshoot their applications on Kubernetes.
- DevOps Engineers: Responsible for building and maintaining Kubernetes infrastructure and CI/CD pipelines.
- System Administrators: Transitioning to cloud-native environments and managing Kubernetes clusters.
- Solution Architects: Designing robust and scalable containerized solutions.
===========================
Today's cloud applications are complex, and traditional monitoring methods can't keep up. This leads to fragmented data, alert overload, and lost productivity for teams. we need more than just a dashboard, we need true observability to understand system behavior and fix problems fast.
This guidebook is designed not just to explain what a unified observability platform is, one that's a scalable and cost-effective alternative to expensive proprietary tools. It integrates metrics, logs, and traces into a single control plane using leading open-source technologies like Grafana, Loki, Tempo, and Mimir.
But what will truly sets us apart is Agentic AI. It's not a basic chatbot; it's an intelligent co-pilot that can reason through telemetry data, create investigative plans, and provide synthesized root cause analysis with clear remediation recommendations.
For Site Reliability Engineering (SRE), DevOps, and platform teams, this means:
i. Natural Language Queries: Simply ask, "Why is the checkout service slow?" and get a detailed answer, not just raw data. ii. Proactive Operations: Agentic AI detects anomalies and anticipates outages before they happen. iii. Faster Recovery: Our platform dramatically reduces Mean Time to Recovery (MTTR), freeing your teams from endless operational burdens.
Observability has become a strategic advantage, transforming IT team from reactive to proactive and paving the way for autonomous, self-healing systems.
- Site Reliability Engineering (SRE), DevOps, and platform teams.