What is the benefit of formal verification in AI security?

Formal verification allows you to mathematically prove that an agent's actions or inputs comply with specific safety theorems, providing a much higher level of assurance than standard heuristic-based filtering.

What is the AI Manipulation Defense System (AIMDS)?

AIMDS is a specialized defense framework that combines temporal analysis, vector pattern matching, and formal mathematical proofs to protect AI agents from manipulation and adversarial attacks.

Can AIMDS be used for production AI agents?

Yes, it is designed for production-grade security, offering high-performance Rust components and optimized vector storage with quantization for minimal latency and memory overhead.

What are the technical requirements for AIMDS?

It requires Node.js 18+, Rust 1.70+, and specific libraries including Midstream, AgentDB v1.6.1, and lean-agentic v0.3.2.

How does AIMDS detect prompt injection?

It uses a three-layer approach: detecting unusual temporal patterns via Midstream, comparing inputs against known attack vectors in AgentDB, and mathematically proving that the input satisfies safety policies using lean-agentic.

AI Manipulation Defense System (AIMDS)

Name: AI Manipulation Defense System (AIMDS)
Author: ricable

byricable

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セキュリティとテスト

Protects AI agents from adversarial attacks and manipulation using temporal analysis, vector intelligence, and formal verification.

概要

AIMDS is a multi-layered security framework designed to harden AI agents against prompt injection, jailbreaking, and behavioral manipulation. By combining Midstream's high-precision temporal pattern detection in Rust with AgentDB's vector search and lean-agentic's formal theorem proving, it provides a robust defense mechanism that mathematically verifies input safety before execution. This skill is essential for developers building production-grade AI systems that require strict policy compliance, resilience against sophisticated adversarial inputs, and high-performance distributed coordination via QUIC protocols.

主な機能

Multi-layered defense combining temporal analysis, vector matching, and formal proofs.
Efficient vector intelligence with 4-32x memory reduction via quantization.
High-performance temporal pattern detection using Rust-based Midstream crates.
Formal verification of safety policies using the lean-agentic theorem prover.
1 GitHub stars
Distributed coordination and synchronization via QUIC multi-stream protocols.

ユースケース

Verifying AI agent decisions against formal mathematical safety policies.
Preventing prompt injection and jailbreak attempts in production LLM applications.
Detecting and blocking adversarial manipulation loops in autonomous agent workflows.

概要

主な機能

Multi-layered defense combining temporal analysis, vector matching, and formal proofs.
Efficient vector intelligence with 4-32x memory reduction via quantization.
High-performance temporal pattern detection using Rust-based Midstream crates.
Formal verification of safety policies using the lean-agentic theorem prover.
1 GitHub stars
Distributed coordination and synchronization via QUIC multi-stream protocols.

ユースケース

Verifying AI agent decisions against formal mathematical safety policies.
Preventing prompt injection and jailbreak attempts in production LLM applications.
Detecting and blocking adversarial manipulation loops in autonomous agent workflows.