Research, engineering deep-dives, and security analysis from the PromptGuard team.
AISI just confirmed that Claude Mythos Preview can complete a full 32-step corporate network attack autonomously. NCSC is telling organizations to assume attackers already have capable AI tools. But nobody is talking about the more immediate threat: what happens when these capabilities are triggered through your own AI agent via prompt injection.
Most AI security tools stop at 'is this request allowed?' That answers one question. Governance answers four harder ones: who is this agent, has its behavior changed, can we prove the audit trail is intact, and can we demonstrate compliance to an auditor?
PromptGuard's multi-layered detection achieves F1 = 0.887 with 99.1% precision across TensorTrust (ICLR 2024), In-the-Wild Jailbreaks (ACM CCS 2024), JailbreakBench (NeurIPS 2024), XSTest (NAACL 2024), and more — with 100% evasion robustness where standalone classifiers achieve only 80%.
A technical analysis of the LiteLLM supply chain attack - how a compromised security scanner led to credential theft across the AI ecosystem, what the three-stage payload did, and what it means for anyone building on LLM infrastructure.
PromptGuard's MCP server works with Cursor, Claude, VS Code Copilot, Windsurf, Cline, Roo Code, Continue, Zed, Goose, Lovable, and every other MCP-compatible tool. Here's how one install protects everything.
OpenClaw is the fastest-growing AI agent framework in history, but its local-first, multi-channel architecture creates a massive attack surface. We break down the CVEs, explain the risks, and show how PromptGuard closes the gaps.
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.
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.
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.
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.
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.
A technical deep dive into how PromptGuard's ensemble of specialized classifiers detects threats — covering parallel inference, weighted voting, category-specific thresholds, confidence calibration, and why multiple small models beat one large one.
PromptGuard is wire-compatible with the OpenAI API. Change one URL and every LLM call in your application is protected by a 14-detector security pipeline. Here's the step-by-step guide for Python, TypeScript, LangChain, and cURL.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
A deep engineering walkthrough of how PromptGuard inspects every prompt in ~150ms using a multi-detector pipeline, ML ensemble, LLM-based jailbreak detection, multi-provider routing, and Redis-backed state — without adding complexity to your codebase.
Using GPT-4 to check if a prompt is safe doubles your latency and your bill. Here's why we bet on a multi-model classical ML ensemble, and how it outperforms single-model approaches at a fraction of the cost.
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.
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.
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.