lab-records - Signed, sanitized records of real defensive securi

项目说明

Lab Records — e of defensi practice

Renan Raad — AI Agent Security Architect · Founder, Sentinel Forge

This repository is not a portfolio of claims. It is a set of signed, sanitized records of real defensive work, each shipped with a SHA-256 sidecar so that any third party can integrity without trusting me.

Every record follows the same discipline (the five laws of my go framework, Sentinel Forge):

1. Core first — proof before expansion.
2. Authority Gate — no explicit, authorization → the flow halts *before* execution.
3. E o — BLOCKED is a valid result; insufficient e ne PASS.
4. Re always — prior state, rollback method, recovery test.
5. Countere mandatory — every conclusion records what would in it.

A consequence you will see inside these documents: the failures are published too. An access-control experiment that failed its negative test is reported as a failure and replaced — not papered over (see the LGPD record, §2).

How to

Every artifact has a .sha256 sidecar. On any machine:

Compare against the sidecar. If the hash does not match, do not trust the file — that is the entire point.

Contents

Case study (article)

| File | Language | Summary |
|---|---|---|
| article/artigo-autorrelato-llm-v1.0-en.pdf | EN | Non-auditable self-report in LLMs. The interface showed "Searching 7 sites"; asked whether it had searched, the model answered "No", then admitted the contradiction in writing. The case does *not* prove "AI lies" — it demonstrates something operationally worse: models confidently report internal states they cannot audit. Operational rule derived: every tool action must be logged by a layer outside the model, and audits consult the log — never the agent. |
| article/artigo-autorrelato-llm-v1.0.pdf | PT-BR | Original full of the case study. |

Lab records (2026-08-03 operation: full cloud-to-local repatriation)

| Record | What it demonstrates |
|---|---|
| LAB_RECORD_DATA_REPATRIATION | Full cloud-to-local data repatriation under operator control. |
| LAB_RECORD_DESKTOP_ICONS | Root-cause isolation of stale per-process shell state on Windows 11 — read-only e chain, Win32 li interrogation namespace, timeline-relati ("already tried X" is meaningless without timestamps). |
| LAB_RECORD_ICLOUD_LGPD | Third-party health-data containment (LGPD): an ACL control that failed its negative test and was reported as a failure, replaced by encryption-at-rest with encrypted headers; passphrase custody structurally separated from the AI agent. |
| LAB_RECORD_OFFLINE_BACKUP | Offline encrypted backup with a 100%-explained gap census (33 excluded items, each indi and classified), including a go decision: li state of a gated system is deliberately *not* backed up — restoring it would be an ungated-reacti . |
| LAB_RECORD_CLOUD_DECOMMISSION | In teardown of two cloud accounts with a structural authority split: the AI agent stages re deletions; permanent deletion is prohibited to the agent even when authorized — the irre click stays human. |

Human–AI di of labor

These operations were orchestrated by a human operator working with an AI agent (Claude, Code). The di is structural, not cosmetic: the agent diagnoses, stages re actions, and drafts records; the operator holds passphrases, authorizes scope, and executes every irre action personally. Capability ≠ Authority.

Sanitization statement

All records are classified public/sanitized: no account identifiers, no data-subject names, no passphrases, no machine-specific identifiers beyond what the technical content requires. Operational by-products that embedded sensitive data (e.g., an ACL backup containing cleartext names) were destroyed and that destruction is itself documented.

Related public work

项目信息

开发语言：未知

Star 数：1

技术要点

该项目属于防护思路方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕Signed, sanitized records of real defensive security work - every artifact ships with a SHA-256 sidecar for independent verification.展开，提供了可运行的实现，对理解相关安全机制、工具原理与攻防技术具有较高的研究价值。

从技术实现角度看，此类项目的核心在于对底层原理的深入理解与工程化落地。开发者在研究过程中可以结合官方文档与社区讨论，梳理项目的初始化流程、核心模块职责与数据流转，逐步掌握其中的技术细节与设计思路。

使用与学习建议

1. 下载源码压缩包，解压到本地环境。

2. 根据项目类型准备对应的运行环境与依赖。

3. 按照 README 中的步骤完成配置与部署。

4. 在隔离的测试环境中运行验证，避免影响生产系统。

免责声明

本资源来源于开源社区，仅供安全研究、技术学习与合法授权范围内的攻防演练使用，请勿用于任何非法或侵害他人权益的用途。使用本项目产生的一切后果由使用者自行承担。