PROMPTGUARD BLOG
Latest news and updates about AI security and PromptGuard.
Loading articles...
The OWASP Top 10 for LLM Applications: A Practitioner's Guide to the Risks That Actually Matter
OWASP published the definitive list of security risks for LLM applications. We've seen every one of them exploited in production. Here's what the list gets right, what it underemphasizes, and the engineering decisions that determine whether each risk becomes a headline.
Beyond Redaction: Why We Replace PII With Synthetic Data
Redacting PII with [SSN_REDACTED] breaks the LLM's ability to reason about data. Replacing it with realistic-looking fake data preserves the reasoning while eliminating the privacy risk. Here's how synthetic data replacement works and when to use it.
From Alert to Action: Setting Up Real-Time Threat Notifications With Webhooks
When PromptGuard blocks a prompt injection at 2 AM, you need to know about it—in Slack, not in an email you'll read tomorrow. Here's how to configure webhook alerting with Slack-compatible payloads and build a threat response workflow.
Multi-Provider Failover: How to Keep Your AI App Running When OpenAI Goes Down
When OpenAI has a 30-minute outage, your AI application doesn't have to go down with it. Here's how PromptGuard's SmartRouter automatically fails over across providers—OpenAI, Anthropic, Gemini, Mistral, Groq, and Azure—without your users noticing.
Shadow Mode: How to Test AI Security Changes Without Breaking Production
Deploying a new security model is terrifying—what if it blocks your best customers? Shadow mode runs the new config alongside production on live traffic, logs disagreements, and lets you validate changes before they affect a single user.
Inside Our 5-Model ML Ensemble: How We Detect Attacks Without Adding Latency
A technical deep dive into how PromptGuard's ensemble of Llama-Prompt-Guard, DeBERTa, ALBERT, toxic-bert, and RoBERTa classifies threats—covering parallel inference, weighted voting, category-specific thresholds, confidence calibration, and why five small models beat one large one.
Secure Your AI App in 5 Minutes: The Complete Integration Guide
PromptGuard is wire-compatible with the OpenAI API. Change one URL and every LLM call in your application is protected by a 7-detector security pipeline. Here's the step-by-step guide for Python, TypeScript, LangChain, and cURL.
Why Your AI Security Should Run in Your VPC (And How to Set It Up)
Sending your user prompts to a security vendor defeats the purpose of security. Here's why we built PromptGuard to be self-hostable first, and a complete guide to deploying it in your own infrastructure.
Securing LangChain Applications: The Complete Guide
LangChain makes it easy to build powerful agents. It also makes it easy to build security vulnerabilities. Here's how to add production-grade security to your chains, agents, and RAG pipelines without rewriting your application.
PCI-DSS for AI: Don't Let Your Chatbot Touch Credit Cards
The moment your AI agent sees a credit card number, your entire compliance scope explodes. Here's how to architect AI-powered financial services that keep PANs out of the LLM context, pass PCI audits, and actually work.
HIPAA and LLMs: The Engineering Guide to Compliant AI in Healthcare
Compliance teams say 'we can't use AI.' Engineering teams say 'just sign a BAA.' Both are wrong. Here's the data minimization architecture that lets you ship HIPAA-compliant AI applications without building your own GPU cluster.
The $50,000 Prompt Injection: A Cost Anatomy of AI Security Breaches
We red-teamed a client's support bot and extracted a $50,000 refund in four hours. Here's the full cost breakdown of an AI security breach—direct losses, forensics, downtime, reputation damage, and the 'Denial of Wallet' attack nobody talks about.
Why Support Bots Are Your Biggest Security Hole (And How to Fix It)
We've watched helpfully trained bots email transaction histories to strangers, issue unauthorized refunds, and leak internal system prompts—all without a single 'jailbreak' keyword. Here's the three-layer defense architecture that actually secures customer support AI.
Your RAG Pipeline Is a Remote Code Execution Vulnerability
You're pulling untrusted HTML, PDFs, and database records into your LLM's context window. If you aren't scanning them for hidden instructions, you're running arbitrary code—written by strangers—inside your most sensitive system.
False Positives Are the Silent Killer of AI Adoption
Blocking a real user is worse than missing an attack. Here's how we reduced our false positive rate from 2.4% to under 0.1% using confidence calibration, feedback loops, and a weekly recalibration pipeline.
Radical Transparency: Why Every Security Decision Needs a Receipt
Most security tools return '403 Forbidden' and leave you guessing. We return the confidence score, the threat type, the event ID, and the source code. Here's why transparency isn't a nice-to-have—it's the only way to build trust.
How We Detect 39+ Types of PII With Layered Regex, Checksum Validation, and ML NER
PII detection is easy if you don't care about false positives. If you do, it's a nightmare. Here's how we built a high-precision PII detector using layered regex, Luhn and checksum validation, ML-based named entity recognition, encoded PII detection, preset-based sensitivity, and synthetic data replacement.
You Can't Regex Your Way Out of Prompt Injection
A customer's chatbot dumped its system prompt when a user asked nicely in French. Here's why keyword filters fail, the defense-in-depth architecture that actually works, and a security checklist for every LLM application.
Building Secure AI Agents: A Default-Deny Architecture
We gave an AI agent permission to 'clean up temp files.' It followed a symlink and deleted 3 months of production logs. Here's the architecture we built to prevent autonomous agents from causing irreversible damage.
Zero-Friction AI Security: From Vulnerable to Protected in One Command
Security that requires manual code changes is security that gets skipped. Here's how we designed PromptGuard's integration model so you can secure an entire codebase by changing one configuration—or one line of code.
Inside PromptGuard's Architecture: How We Built a Production AI Firewall
A deep engineering walkthrough of how PromptGuard inspects every prompt in ~150ms using a 7-detector pipeline, 5-model ML ensemble, LLM-based jailbreak detection, multi-provider routing, and Redis-backed state—without adding complexity to your codebase.
Why We Don't Use LLMs to Secure LLMs
Using GPT-4 to check if a prompt is safe doubles your latency and your bill. Here's why we bet on a 5-model classical ML ensemble, and how it outperforms single-model approaches at a fraction of the cost.
Anatomy of Prompt Injection: Attack Patterns We See in Production
We sit between thousands of apps and their LLM providers. Here are the five categories of prompt injection attacks we block regularly, how each one works, and why they're harder to stop than you think.
Automated Red Teaming: How to Break Your AI Before Your Users Do
You wouldn't ship code without tests. Why are you shipping AI prompts without adversarial testing? Here's how we built a 20-vector red team engine into the gateway, and how to use it to find your blind spots before production.
Why We Built a Transparent AI Firewall
We built PromptGuard because we were tired of black-box security tools that blocked legitimate users without explanation. Here's why we designed it for transparency and auditability, how it works, and what we learned building an AI firewall that developers don't hate.