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Home Security AI Benchmark

A test suite for AI systems used in home security. It checks language and vision models across tasks such as scene analysis, security classification, alert handling, tool use, and resistance to malicious instructions.

In plain words
What is it for?
Use it to evaluate an AI gateway with 143 tests in 16 suites, with optional tests that inspect images. It runs a benchmark script and generates an HTML report.
Why use it?
It provides structured tests for finding weaknesses in a home-security AI before deployment. The tests cover both recognising situations and turning them into appropriate alerts or actions.

Skill for Claude CodeCodex

3.0k +6 2mo ago A scan 17 tokens original MIT
View the source file SharpAI/DeepCamera
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/sharpai/deepcamera/home-security-benchmark
Any agent
npx skills add SharpAI/DeepCamera --skill home-security-benchmark
Clone the repo
git clone --depth 1 https://github.com/SharpAI/DeepCamera

Made for: Claude Code, Codex.

Per session 17 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,566 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00017 $0.01566
Opus 5 $0.00009 $0.00783
Sonnet 5 $0.00003 $0.00313
Haiku 4.5 $0.00002 $0.00157

Measured 2d ago against content hash 6a5d5a168495, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Home Security AI Benchmark scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/generate-report.cjs, scripts/run-benchmark.cjs, scripts/test-model-config.cjs), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/analysis/home-security-benchmark/SKILL.md · 146 lines

How it starts

The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Home Security AI Benchmark

Comprehensive benchmark suite evaluating LLM and VLM models on 143 tests across 16 suites — context preprocessing, tool use, security classification, prompt injection resistance, alert routing, knowledge injection, VLM-to-alert triage, and scene analysis.

Setup

Requires npm install. This skill has a package.json with dependencies (e.g. openai SDK). The deployment agent should check for package.json and run npm install in the skill directory before launching.

Entry script: scripts/run-benchmark.cjs

Verification

node scripts/run-benchmark.cjs --help

Quick Start

As an Aegis Skill (automatic)

When spawned by Aegis, all configuration is injected via environment variables. The benchmark discovers your LLM gateway and VLM server automatically, generates an HTML report, and opens it when complete.

Standalone

# LLM-only (VLM tests skipped)
node scripts/run-benchmark.cjs

# With VLM tests (base URL without /v1 suffix)
node scripts/run-benchmark.cjs --vlm http://localhost:5405

# Custom LLM gateway
node scripts/run-benchmark.cjs --gateway http://localhost:5407

# Skip report auto-open
node scripts/run-benchmark.cjs --no-open

Configuration

Environment Variables (set by Aegis)

Variable Default Description
AEGIS_GATEWAY_URL http://localhost:5407 LLM gateway (OpenAI-compatible)
AEGIS_LLM_URL — Direct llama-server LLM endpoint
AEGIS_LLM_API_TYPE openai LLM provider type (builtin, openai, etc.)
AEGIS_LLM_MODEL — LLM model name
AEGIS_LLM_API_KEY — API key for cloud LLM providers
AEGIS_LLM_BASE_URL — Cloud provider base URL (e.g. https://api.openai.com/v1)
AEGIS_VLM_URL (disabled) VLM server base URL
AEGIS_VLM_MODEL — Loaded VLM model ID
AEGIS_SKILL_ID — Skill identifier (enables skill mode)
AEGIS_SKILL_PARAMS {} JSON params from skill config

Read the full file on GitHub · 146 lines

Files

What ships with it

55 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 146 lines · 17 tokens per session scan A 6a5d5a168495

Subscribe to this mod's changes

Home Security AI Benchmark is a skill published in the GitHub repository SharpAI/DeepCamera (3,031 stars, last pushed 2mo ago), licensed MIT. It adds 17 tokens to every session and 1,566 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

SecurityTestingaianalysisdeep-learningface-recognitionmachine-learningobject-detectionvideo-surveillance
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