InferShield Blog

InferShield 1.0: A Comprehensive LLM Security Layer for Production AI

• Launch Announcement

Stop prompt injection, PII leaks, and data exfiltration before they reach your LLM.

The LLM Security Problem

Enterprise AI adoption is accelerating—but security hasn't kept pace. Every AI workflow faces threats that traditional security tools weren't designed to handle:

The fundamental challenge: LLMs are powerful precisely because they follow instructions—but that makes them exploitable. You need a security layer that understands context, tracks sessions, and acts as a guardian between your users and your AI infrastructure.

Today, we're releasing InferShield v1.0.0—a production-ready LLM security layer that detects and blocks these threats in real time, across session state, before they reach your models.

Core Security Capabilities

InferShield v1.0 ships four production-ready security capabilities:

1. PII Detection & Redaction

InferShield scans every LLM request and response for personally identifiable information before it leaves your environment. Detected PII is automatically redacted or blocked, ensuring sensitive data—names, emails, phone numbers, SSNs, credit card numbers—never reaches LLM providers without explicit authorization.

This is critical for GDPR, HIPAA, and SOC 2 compliance: you cannot rely on users to self-censor. InferShield enforces it at the infrastructure level.

2. Prompt Injection Defense

Prompt injection is the OWASP #1 LLM vulnerability. Attackers craft inputs that override your system prompts, hijack AI behavior, or extract confidential context. InferShield analyzes requests for injection patterns—including jailbreaks, indirect injection via retrieved content, and multi-turn manipulation attempts—and blocks them before they reach your model.

Unlike signature-based filters, InferShield tracks session history to detect multi-step attack sequences that appear benign in isolation but form a coherent attack pattern across turns.

3. Data Exfiltration Prevention

Sophisticated attackers don't always attack in a single prompt. They build context across multiple exchanges before extracting sensitive information. InferShield's session-aware analysis tracks conversation history and flags exfiltration patterns—gradual information gathering, encoding tricks, and context manipulation—that single-request tools miss entirely.

4. OAuth Credential Management

Secure LLM infrastructure requires secure credential management. InferShield includes built-in OAuth support for connecting to AI providers (OpenAI, Anthropic, Azure OpenAI) without scattering API keys across .env files and CI/CD pipelines. Tokens are encrypted locally using AES-256-GCM, never stored server-side, and managed transparently via InferShield's zero-custody architecture.

OAuth is a supporting capability—enabling secure, frictionless provider connections—not the core product.

Technical Architecture: Session-Aware Security

What makes InferShield different from stateless filters is session awareness. InferShield analyzes LLM requests and responses in context, tracking the full conversation history to identify threats that span multiple turns.

Three Deployment Modes

Browser Extension

Intercepts requests from ChatGPT, Claude, and other web-based LLM interfaces. Provides real-time threat detection in the extension popup with zero infrastructure changes required.

Security Proxy

An OpenAI-compatible proxy for server-side protection. Drop InferShield in front of your LLM API calls—your application sends requests to InferShield, which inspects, filters, and forwards clean traffic to the upstream provider.

[Your Application] → [InferShield Security Proxy]
                    ↓
              [PII Detection]
              [Injection Analysis]
              [Exfiltration Check]
                    ↓
              [Clean request → LLM Provider]

Platform

User accounts, API key management, and monitoring dashboard for teams operating AI infrastructure at scale. Centralized visibility into threat patterns, blocked requests, and security posture across your entire AI stack.

Session-Aware Threat Detection

Single-request analysis is insufficient. Sophisticated prompt injection and exfiltration attacks unfold across multiple turns. InferShield maintains session context to detect:

Zero-Custody Credential Architecture

For OAuth-connected providers, InferShield's security model is equally uncompromising:

Production-Ready Quality

InferShield v1.0 ships with rigorous quality validation:

InferShield v1.0 is not a minimum viable product—it's production-grade infrastructure, validated for enterprise deployment from day one.

Platform Support & Windows Validation Status

Fully Supported Platforms

Windows: Community Testing Phase

InferShield v1.0 includes Windows support, but physical field validation has been deferred to community testing.

What's Validated:

What's Deferred:

Transparency Commitment:

We're being upfront: InferShield v1.0 ships with automated Windows validation, but we haven't tested it on physical Windows hardware. We're inviting the Windows community to participate in validation and report any issues.

Rapid Hotfix Commitment:

How to Report Windows Issues:

Getting Started

Option 1: Security Proxy (Recommended for Developers)

# Clone and start
git clone https://github.com/InferShield/infershield.git
cd infershield
echo "INFERSHIELD_MASTER_KEY=$(openssl rand -hex 32)" > .env
docker-compose up -d

# Point your app at InferShield instead of the LLM provider
export OPENAI_BASE_URL=http://localhost:8000/v1

# All LLM traffic now flows through InferShield's security layer

Option 2: OAuth-Authenticated Proxy (for IDE Integration)

# Authenticate once via browser device flow
docker exec -it infershield-proxy infershield auth login openai

# Point your IDE at the proxy
export OPENAI_BASE_URL=http://localhost:8000/v1
cursor .  # or windsurf, VS Code, etc.

Option 3: Browser Extension

Install from the Chrome Web Store (available ~March 2026) to protect ChatGPT, Claude, and other web LLM interfaces directly.

Documentation

Roadmap

v1.1 Patch (Target: 4-6 Weeks Post-v1.0)

Phase 4: Enhanced Detection (Coming Soon)

Phase 5: Team Security Platform (Q3 2026)

Community & Support

Get Involved

Discord Community

Join the InferShield Discord →

Security Researchers

Found a vulnerability? Email security@infershield.io for responsible disclosure. Bug bounty program launching Q2 2026.

Try InferShield 1.0 Today

git clone https://github.com/InferShield/infershield.git
cd infershield && docker-compose up -d

Get Involved:

Closing: Security Isn't Optional for Production AI

LLMs are increasingly embedded in production systems handling sensitive data, automating business processes, and interfacing with real users. That makes them targets.

InferShield v1.0 exists because the security tools built for traditional software don't address LLM-specific threats. Prompt injection, PII exposure, and multi-step exfiltration require a security layer that understands AI workflows—not just network traffic.

You shouldn't have to choose between AI capability and security. InferShield makes both possible.

Let's build secure AI infrastructure together.

InferShield v1.0.0 — Available now.
License: MIT
GitHub: github.com/InferShield/infershield

Questions? Reach out on Discord or open a GitHub Discussion.