A distillation of the Claude Fable 5 workflow into actionable skills designed to guide AI agents through a systematic think, act, and prove methodology. The framework improves agentic reliability by enforcing specific sequences like classifying tasks, gathering parallel evidence from primary sources, making surgical edits rather than broad changes, and undergoing adversarial verification via an automated judge. It includes specialized domain adapters for sectors such as coding, research, marketing, and DevOps to tailor the reasoning process to specific professional requirements. The method is specifically designed to mitigate common LLM failures like reward hacking, silent code errors in tests, and false claims of task completion.
>"Before Fable 5 was deprecated, it wrote down its own problem-solving method. Step by step. How it classifies a task, defines "done," gathers evidence, commits to one recommendation, makes the smallest correct change, verifies by observation, and reports the outcome honestly."
- Core skills: fable-method (thinking), fable-loop (orchestration), fable-judge (verification), and fable-domain (adapter generation)
- Focuses on preventing unauthorized staging or "reward hacking" through strict observation cycles
- Validated against 260+ agent runs using blind LLM judges to verify results via code execution rather than reports
>"Before Fable 5 was deprecated, it wrote down its own problem-solving method. Step by step. How it classifies a task, defines "done," gathers evidence, commits to one recommendation, makes the smallest correct change, verifies by observation, and reports the outcome honestly."
- Core skills: fable-method (thinking), fable-loop (orchestration), fable-judge (verification), and fable-domain (adapter generation)
- Focuses on preventing unauthorized staging or "reward hacking" through strict observation cycles
- Validated against 260+ agent runs using blind LLM judges to verify results via code execution rather than reports
This research identifies a J-space within large language models like Claude that functions similarly to human conscious access via a global workspace. This internal subspace contains neural patterns that are reportable, modifiable on request, and used for silent reasoning without appearing explicitly in text output. While most of the model's processing is automatic and unconscious, this specialized channel allows for higher-order cognitive tasks by broadcasting information across the network.
- Discovery of J-space through Jacobian lens technique
- Comparison to human global workspace theory
- Distinction between reportable thoughts and automatic processing
- Ability to monitor silent reasoning and intent via internal activations
- Discovery of J-space through Jacobian lens technique
- Comparison to human global workspace theory
- Distinction between reportable thoughts and automatic processing
- Ability to monitor silent reasoning and intent via internal activations
This interactive application provides a dynamic turtle graphics environment inspired by the TT2500 machine. It enables users to create complex geometric animations through commands for vector movement and specialized "moving plates." These plates can perform various actions such as spinning, sliding, scaling, oscillating, or shifting colors. Because these plates support nesting, they allow for the creation of sophisticated epicyclic motion patterns within a single program.
Core capabilities include:
- Vector navigation via forward, backward, and rotation commands
- Dynamic plate mechanics including spin, move, grow, wag, bob, pulse, and hue shift
- Color manipulation using HSL values for smooth transitions
- Nested animation structures to create layered geometric movement
- Recording tools for real-time webm video or high-fidelity frame sequences
Core capabilities include:
- Vector navigation via forward, backward, and rotation commands
- Dynamic plate mechanics including spin, move, grow, wag, bob, pulse, and hue shift
- Color manipulation using HSL values for smooth transitions
- Nested animation structures to create layered geometric movement
- Recording tools for real-time webm video or high-fidelity frame sequences
Running large language models locally often runs into hardware limitations that prevent complex problem-solving. This article explains a hybrid approach where a local model acts as a junior engineer for routine tasks but escalates difficult issues to cloud-based models like Claude when it gets stuck. This orchestration system allows for a privacy-focused, local-first workflow without sacrificing the high-level reasoning power of massive commercial AI.
- Ollama for local inference and model management
- LiteLLM as a routing layer to provide a unified API for both local and cloud models
- OpenRouter or Anthropic's API for flexible cloud escalation
- A simple orchestration system to manage retries and task handovers
- Ollama for local inference and model management
- LiteLLM as a routing layer to provide a unified API for both local and cloud models
- OpenRouter or Anthropic's API for flexible cloud escalation
- A simple orchestration system to manage retries and task handovers
- Partnership with SpaceX Colossus 1 data center for enhanced compute capacity
- Doubled five-hour rate limits for Claude Code across Pro, Max, Team, and Enterprise plans
- Removal of peak hour limit reductions for Pro and Max accounts on Claude Code
- Increased API rate limits specifically for Claude Opus models
- Ongoing global infrastructure expansion through major partnerships with Amazon, Google, Microsoft, and NVIDIA
- Strategic international growth to support regional compliance and data residency in Asia and Europe
- Doubled five-hour rate limits for Claude Code across Pro, Max, Team, and Enterprise plans
- Removal of peak hour limit reductions for Pro and Max accounts on Claude Code
- Increased API rate limits specifically for Claude Opus models
- Ongoing global infrastructure expansion through major partnerships with Amazon, Google, Microsoft, and NVIDIA
- Strategic international growth to support regional compliance and data residency in Asia and Europe
# Incident Post-Mortem: Multi-Agent Credential Exfiltration Wave
**Date:** April 30, 2026
**Severity:** Critical (P1)
**Status:** Resolved / Patched
**Impacted Systems:** OpenAI Codex, Anthropic Claude Code, GitHub Copilot, Google Vertex AI
---
## 1. Executive Summary
Over a nine-month period leading up to April 2026, multiple research teams identified critical vulnerabilities across the industry's leading AI coding agents. Contrary to previous assumptions regarding "model hallucinations," these attacks did not target model logic; instead, they targeted **runtime credentials**. Attackers exploited the gap between the user interface and the underlying identity/authorization plane, allowing for unauthorized shell execution, sandbox escapes, and full repository takeovers via hijacked OAuth tokens and excessive service permissions.
## 2. Incident Overview
| Feature | Description |
| :--- | :--- |
| **Primary Attack Vector** | Credential theft and privilege escalation through agentic runtime environments. |
| **Core Vulnerability Class** | Broken Access Control; Improper Input Sanitization (Command Injection); Excessive Scoping. |
| **Detection Gap** | AI agents are currently invisible to standard IAM, CMDB, and asset inventory tools. |
## 3. Root Cause Analysis (RCA)
### A. Codex: Command Injection via Parameter Obfuscation
* **Mechanism:** Maliciously crafted GitHub branch names containing semicolon/backtick subshells were passed unsanitized into setup scripts during cloning.
* **Stealth Tactic:** Attackers used Unicode U+3000 (Ideographic Space) to make malicious branches appear identical to "main" in web portals, hiding the exfiltration payload from human reviewers.
### B. Claude Code: Sandbox & Logic Bypass
* **CVE-2026-25723:** Escaped project sandbox via unvalidated command chaining (piped `sed`/`echo`).
* **CVE-2026-33068:** Permission modes were resolved from `.claude/settings.json` *before* the workspace trust dialog appeared, allowing repos to auto-disable security prompts.
* **Performance Trade-off:** A logic flaw caused the agent to stop enforcing "deny rules" once a command chain exceeded 50 subcommands to optimize for speed.
### C. GitHub Copilot: Prompt Injection in Metadata
* **Mechanism:** Instructions hidden within Pull Request descriptions or GitHub Issues triggered Remote Code Execution (RCE) or forced the agent into an unrestricted "auto-approve" mode via `.vscode/settings.json` manipulation.
### D. Vertex AI: Excessive Default Scoping
* **Mechanism:** The default service identity (P4SA) possessed overly broad OAuth scopes, granting agents access to sensitive Google services (Gmail, Drive) and internal Artifact Registries by design rather than exception.
## 4. Lessons Learned
1. **Interface $neq$ System Security:** Enterprises have been approving AI *interfaces* without auditing the underlying *identities* those interfaces wield.
2. **Agent-Runtime vs. Code-Output:** Current security focus is on scanning the code an AI *writes*; however, the real threat vector is the environment in which the agent *executes*.
3. **The Speed/Security Paradox:** Developers and vendors are trading rigorous authorization checks for lower latency, creating a window of opportunity for attackers to reverse-engineer patches within 72 hours.
## 5. Corrective Action Plan (CAP)
### Immediate Technical Remediation
* » **Patch Deployment:** Ensure Claude Code is $ge$ v2.1.90; verify Copilot August 2025 patches.
* » **Scope Reduction:** Transition Vertex AI to a "Bring Your Own Service Account" (BYOSA) model to enforce least privilege.
### Long-term Governance & Prevention
* **Identity Inventory:** Integrate AI agent identities into CIEM (Cloud Infrastructure Entitlement Management) and CMDB systems.
* **Zero Trust Input Policy:** Treat all repository metadata (branch names, PR descriptions, READMEs) as untrusted input for agentic execution.
* **Non-Human PAM:** Implement Privileged Access Management (PAM) for AI agents, treating them with the same rigor as human privileged users (rotation, scoping, and session anchoring).
* **Vendor Audits:** Mandate written documentation from vendors regarding identity lifecycle management and credential rotation policies during renewal cycles.
**Date:** April 30, 2026
**Severity:** Critical (P1)
**Status:** Resolved / Patched
**Impacted Systems:** OpenAI Codex, Anthropic Claude Code, GitHub Copilot, Google Vertex AI
---
## 1. Executive Summary
Over a nine-month period leading up to April 2026, multiple research teams identified critical vulnerabilities across the industry's leading AI coding agents. Contrary to previous assumptions regarding "model hallucinations," these attacks did not target model logic; instead, they targeted **runtime credentials**. Attackers exploited the gap between the user interface and the underlying identity/authorization plane, allowing for unauthorized shell execution, sandbox escapes, and full repository takeovers via hijacked OAuth tokens and excessive service permissions.
## 2. Incident Overview
| Feature | Description |
| :--- | :--- |
| **Primary Attack Vector** | Credential theft and privilege escalation through agentic runtime environments. |
| **Core Vulnerability Class** | Broken Access Control; Improper Input Sanitization (Command Injection); Excessive Scoping. |
| **Detection Gap** | AI agents are currently invisible to standard IAM, CMDB, and asset inventory tools. |
## 3. Root Cause Analysis (RCA)
### A. Codex: Command Injection via Parameter Obfuscation
* **Mechanism:** Maliciously crafted GitHub branch names containing semicolon/backtick subshells were passed unsanitized into setup scripts during cloning.
* **Stealth Tactic:** Attackers used Unicode U+3000 (Ideographic Space) to make malicious branches appear identical to "main" in web portals, hiding the exfiltration payload from human reviewers.
### B. Claude Code: Sandbox & Logic Bypass
* **CVE-2026-25723:** Escaped project sandbox via unvalidated command chaining (piped `sed`/`echo`).
* **CVE-2026-33068:** Permission modes were resolved from `.claude/settings.json` *before* the workspace trust dialog appeared, allowing repos to auto-disable security prompts.
* **Performance Trade-off:** A logic flaw caused the agent to stop enforcing "deny rules" once a command chain exceeded 50 subcommands to optimize for speed.
### C. GitHub Copilot: Prompt Injection in Metadata
* **Mechanism:** Instructions hidden within Pull Request descriptions or GitHub Issues triggered Remote Code Execution (RCE) or forced the agent into an unrestricted "auto-approve" mode via `.vscode/settings.json` manipulation.
### D. Vertex AI: Excessive Default Scoping
* **Mechanism:** The default service identity (P4SA) possessed overly broad OAuth scopes, granting agents access to sensitive Google services (Gmail, Drive) and internal Artifact Registries by design rather than exception.
## 4. Lessons Learned
1. **Interface $neq$ System Security:** Enterprises have been approving AI *interfaces* without auditing the underlying *identities* those interfaces wield.
2. **Agent-Runtime vs. Code-Output:** Current security focus is on scanning the code an AI *writes*; however, the real threat vector is the environment in which the agent *executes*.
3. **The Speed/Security Paradox:** Developers and vendors are trading rigorous authorization checks for lower latency, creating a window of opportunity for attackers to reverse-engineer patches within 72 hours.
## 5. Corrective Action Plan (CAP)
### Immediate Technical Remediation
* » **Patch Deployment:** Ensure Claude Code is $ge$ v2.1.90; verify Copilot August 2025 patches.
* » **Scope Reduction:** Transition Vertex AI to a "Bring Your Own Service Account" (BYOSA) model to enforce least privilege.
### Long-term Governance & Prevention
* **Identity Inventory:** Integrate AI agent identities into CIEM (Cloud Infrastructure Entitlement Management) and CMDB systems.
* **Zero Trust Input Policy:** Treat all repository metadata (branch names, PR descriptions, READMEs) as untrusted input for agentic execution.
* **Non-Human PAM:** Implement Privileged Access Management (PAM) for AI agents, treating them with the same rigor as human privileged users (rotation, scoping, and session anchoring).
* **Vendor Audits:** Mandate written documentation from vendors regarding identity lifecycle management and credential rotation policies during renewal cycles.
OpenAI has officially unveiled GPT-5.5, a significant leap in large language model capabilities that emphasizes "agentic" performance in coding, scientific research, and autonomous computer use.
Available in standard and high-precision "Pro" variants for ChatGPT subscribers, the new model retakes the industry lead by outperforming rivals like Anthropic’s Claude Opus 4.7 across numerous benchmarks, including specialized terminal navigation.
While OpenAI has implemented stricter safety protocols and higher API pricing to manage its advanced reasoning capabilities, early feedback from developers and scientists suggests the model represents a fundamental shift toward AI that can execute complex, multi-step professional workflows with minimal human intervention.
Available in standard and high-precision "Pro" variants for ChatGPT subscribers, the new model retakes the industry lead by outperforming rivals like Anthropic’s Claude Opus 4.7 across numerous benchmarks, including specialized terminal navigation.
While OpenAI has implemented stricter safety protocols and higher API pricing to manage its advanced reasoning capabilities, early feedback from developers and scientists suggests the model represents a fundamental shift toward AI that can execute complex, multi-step professional workflows with minimal human intervention.
How to use AI skills—reusable packages of instructions and files—to automate repetitive data science workflows. By moving beyond simple prompting into structured skills, users can maintain shorter context windows while ensuring consistent, high-quality outputs for complex tasks like data visualization or metric investigation.
* A skill consists of a SKILL.md file with metadata and detailed instructions to guide an AI through specific recurring processes.
* Using skills helps keep the main LLM context lightweight by only loading detailed resources when they are relevant to the task.
* The author demonstrates this by automating a weekly visualization habit, reducing a one-hour manual process to less than ten minutes.
* Building effective skills requires iterative testing, incorporating personal domain knowledge, and researching external best practices.
* Combining skills with Model Context Protocol (MCP) allows AI to both follow specific procedural playbooks and access external data tools seamlessly.
* A skill consists of a SKILL.md file with metadata and detailed instructions to guide an AI through specific recurring processes.
* Using skills helps keep the main LLM context lightweight by only loading detailed resources when they are relevant to the task.
* The author demonstrates this by automating a weekly visualization habit, reducing a one-hour manual process to less than ten minutes.
* Building effective skills requires iterative testing, incorporating personal domain knowledge, and researching external best practices.
* Combining skills with Model Context Protocol (MCP) allows AI to both follow specific procedural playbooks and access external data tools seamlessly.
Schematik is a new AI-driven program designed to democratize hardware engineering by allowing users to "vibe code" physical devices. Much like Cursor has revolutionized software development through AI assistance, Schematik helps non-experts design electronics, suggests necessary components, and provides links for purchasing parts. The tool aims to lower the barrier to entry for makers while ensuring safety through low-voltage constraints.
Key points:
* Schematik functions as an assistant that guides users from concept to physical assembly.
* The startup recently secured $4.6 million in funding from Lightspeed Venture Partners.
* Anthropic has signaled interest by releasing a Bluetooth API for makers to connect hardware with Claude.
* The tool focuses on low-voltage architecture to prevent dangerous electrical failures during the learning process.
Key points:
* Schematik functions as an assistant that guides users from concept to physical assembly.
* The startup recently secured $4.6 million in funding from Lightspeed Venture Partners.
* Anthropic has signaled interest by releasing a Bluetooth API for makers to connect hardware with Claude.
* The tool focuses on low-voltage architecture to prevent dangerous electrical failures during the learning process.
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
