Evergreen Overview
Application architecture, developer workflow, tooling, and production patterns for building AI systems.
AI Engineering
Application architecture, developer workflow, tooling, and production patterns for building AI systems.
ai engineeringllm application developmentagent engineeringmcpdeveloper toolingai systems
What this page covers
- Core concepts for ai engineering
- Useful references, notes, and curated examples
- Practical links back to AI systems and operational risk
Why it matters
- It creates better language for technical and governance discussions
- It helps teams connect theory to deployed workflows
- It supports more repeatable review and decision-making
Who this page is for
- Researchers and builders working with AI systems
- Security and governance teams
- Leaders looking for current reference material
References
Current notes, events, and source material
These items are included because they add useful evidence, framing, implementation detail, or upcoming context for teams working in this area.
Now in preview: Find and fix software vulnerabilities with CodeMender
Google opened a preview of CodeMender, an AI code-security agent delivered through Gemini Enterprise Agent Platform and AI Threat Defense. It is designed to inspect code, identify and validate potentially exploitable defects, and produce targeted fixes, with Google’s specialized Gemini 3.5 Flash Cyber model initially restricted to governments and trusted partners.
Authenticate legitimate AI agent traffic with AWS WAF Bot Control
AWS provides a four-step technical guide to authenticating automated agents with Web Bot Authentication: deploy WAF Bot Control, sign requests with Ed25519 HTTP Message Signatures, write rules against verification labels, and monitor attempts through WAF logs and CloudWatch.
Securing the future of AI agents
Google DeepMind frames increasingly capable agents as potential insider threats and proposes an AI Control Roadmap that combines access controls with supervisors that inspect plans, reasoning, and actions. Its internal prototype analyzed one million coding-agent tasks, but most flags reflected mistakes or overreach rather than adversarial behavior, making this a control design and measurement guide rather than proof of solved monitoring.
NVIDIA Forms 37-Member Open Secure AI Alliance and Open-Sources NOOA Framework
NVIDIA launched the Open Secure AI Alliance and contributed NOOA, an Apache-2.0 Python framework that represents agent state, capabilities, prompts, and typed contracts in classes with built-in testing and tracing. NVIDIA reports 86.8% on CyberGym L1 with GPT-5.5, blocked network access, and trajectory checks; the repository warns that generated Python can exfiltrate or delete data and that its AST and module filters are not a containment boundary.
OWASP ASI02: tool misuse and exploitation — the definitive security guide
This OWASP ASI02 guide separates accidental and adversarial tool misuse across misinterpreted requests, ignored constraints, poisoned tool descriptions, supply-chain injection, and unsafe multi-tool chains. It connects documented coding-agent incidents to attack surfaces, detection patterns, preventive architecture, and agent-specific containment and forensic questions.
OWASP AIBOM Generator
The OWASP AIBOM Generator creates CycloneDX-aligned inventories for Hugging Face models, visualizes model metadata and dependencies, and scores field completeness. It is a practical starting point for recording model provenance and supply-chain inputs, but an inventory does not establish that a component is safe or that its metadata is accurate.
The Wiz Red Agent is Now Generally Available
Wiz launched Red Agent for continuous application and API penetration testing. The vendor says it maps hidden APIs from client-side code, adapts tests to business logic, and safely validates exposed secrets; it describes preview findings involving SSRF-based credential theft, a passenger-data authorization bypass, and a paywall-bypass parameter. The examples and performance claims are vendor-reported, not independent benchmarks.
Introducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber
Google introduced Gemini 3.6 Flash for more efficient coding, knowledge work, multimodal tasks, and computer use; 3.5 Flash-Lite for high-throughput, low-latency agent workflows; and 3.5 Flash Cyber for vulnerability research inside CodeMender. Google reports lower token use for 3.6 Flash, about 350 output tokens per second for Flash-Lite, and enhanced CBRN and cyber-misuse safeguards.
Decoupled DiLoCo: A new frontier for resilient, distributed AI training
Google DeepMind’s Decoupled DiLoCo divides model training into asynchronous compute islands across data centers. Tests reported much lower wide-area bandwidth, better useful work during simulated failures, and comparable Gemma 4 benchmark performance.
Advance Zero Trust for AI: New tools and guidance to secure AI agents and DevSecOps
Microsoft added AI, Security Operations, and Infrastructure checks to its Zero Trust Assessment and a DevSecOps pillar with 15 control groups and 91 tasks to its Zero Trust Workshop, alongside new guidance for governing agent memory.
Claude Mythos research prompts HAWK withdrawal and speeds a reduced-round AES attack
Anthropic reports that Claude Mythos Preview helped produce an end-to-end HAWK-256 key-recovery attack and a projected 200- to 800-fold speedup for an attack on seven-round AES-128. Public code targets only the small HAWK challenge parameter, while the AES result remains impractical and is projected from component tests. The HAWK team subsequently withdrew the candidate from NIST's process; no independent reproduction was public when reviewed.
Rethinking Scanning for the AI Era: Wiz’s Agentic Code Security System
Wiz proposes a three-tier application-security architecture: deterministic rules on every change, continuous AI reasoning across repositories and pull requests for logic and data-flow flaws, and expensive agentic pentesting only for high-value applications or risks. Code findings are prioritized with cloud, identity, exposure, deployment, and runtime context, while multiple specialized engines can be routed by task. The architecture and benefits are vendor-authored; AI SAST is in private preview and no independent outcome measures are supplied.
Structuring Applications to Secure the KV Cache
NVIDIA explains how shared prefix caching can create a timing side channel in multitenant LLM services. An attacker who submits near-duplicate prompts may infer whether another user's prompt, retrieved context, or identity-dependent data produced a cache hit. Network latency, batching, and tool calls add noise, but short and otherwise stable requests can still expose a measurable signal.
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BlackIce: A Containerized Red Teaming Toolkit for AI Security Testing
BlackIce packages fourteen open-source responsible-AI, LLM-security, and adversarial-ML tools into a reproducible, version-pinned container with a unified command-line interface. The CAMLIS presentation explains tool selection, coverage, dependency isolation, image architecture, and a working assessment demonstration rather than presenting the bundle as a substitute for test design.
Kimsuky Builds Offline AI Stack to Boost Phishing and Automate Malware Development
Genians linked Kimsuky infrastructure to configured Ollama and GPT4All runtimes, a LocalDocs RAG database, Whisper files, Cursor, and agent-development libraries. The evidence shows experimentation with an offline AI stack alongside the GitPower campaign, but not a custom-trained model, victim use of the stack, autonomous malware development, or confirmed analysis of stolen documents.
Introducing Gemma 4 12B: a unified, encoder-free multimodal model
Google introduced Gemma 4 12B, an Apache 2.0 model that accepts text, vision, and native audio without separate multimodal encoders. It targets local agentic workloads and can run on laptops with 16 GB of memory.
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Importing Phantoms: Measuring LLM Package Hallucination Vulnerabilities
Arjun Krishna and collaborators measure fictional dependency generation across eleven models and Python, JavaScript, and Rust tasks. They find that package-hallucination behavior varies with the model, language, size, and request specificity, creating a supply-chain opening when an attacker registers a plausible package name suggested by an AI coding system.
Researcher Says AI Helped Develop Linux Traffic-Control Race Into Root Exploit
STAR Labs researcher Lee Jia Jie reports using an unnamed AI assistant to identify and weaponize CVE-2026-53264, a Linux network-scheduler use-after-free race, into local root on a targeted CentOS Stream 9 kernel. The exploit needs a local foothold, unprivileged user namespaces, specific traffic-control options, and kernel-specific ROP offsets; upstream fixed the lifetime bug by deferring frees through RCU.
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Why Great Models Fail: Lessons From 9 Years of Deploying ML Models - Megan Robertson
Drawing on nine years of cross-industry ML deployments, Megan Robertson explains why a statistically accurate model can still fail to deliver in production. The session moves beyond offline performance to scoping, organizational failure modes, monitoring, maintainability, and the operational conditions required for a model to keep producing useful results.
Nine AI coding agent incidents that ended with deleted data
A review of nine coding-agent incidents from 2025 and 2026 separates shell, path, and harness failures from constraint decay and excessive permissions, then maps them to concrete controls for identities, filesystems, approvals, verification, logging, and backups.
RufRoot: unauthenticated Ruflo MCP bridge enabled RCE and memory poisoning
Before Ruflo 3.16.3, its default Docker Compose deployment bound the MCP bridge to all interfaces without authentication. A reachable attacker could invoke the terminal tool, read model-provider keys and conversations, spawn agents, and poison persistent AgentDB patterns. Noma Labs verified the chain; the patch adds loopback binding, bearer authentication for public exposure, an opt-in terminal tool, authenticated MongoDB, tighter CORS and container defaults, and regression tests.
Thailand's Ministry of Finance Targeted With Hermes AI Agent Running Unattended
Hunt.io recovered 585 files and Hermes logs from an exposed staging server used against Thailand's Ministry of Finance. The evidence shows an operator who already had target knowledge and access running Hermes in unattended “YOLO” mode for repetitive post-exploitation enumeration, while also staging Hadoop exploitation scripts and a custom Hades implant; it does not show the agent finding the initial entry point or novel vulnerabilities.
TuxBot v3: Inside an IoT Botnet Framework With LLM-Assisted Development
Unit 42 analyzes TuxBot v3 Evolution, a roughly 70%-functional IoT botnet framework with code compiled for 17 architectures. Researchers found raw model reasoning, hallucinated cryptography, and other evidence of unreviewed LLM-generated code in the source.
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Breaking AI Inference Systems: Lessons From Pwn2Own Berlin
Fuzzinglabs researchers explain how threat modeling, file-format fuzzing, and plugin analysis exposed an authentication bypass and memory-corruption issues in Ollama plus command injection in NVIDIA Triton Inference Server's model-configuration pipeline. The Pwn2Own case study also examines RedisAI, ChromaDB, and container-runtime attack surfaces.