Wingback SecuritySolution · AI application security
Security for the AI you ship
When your own engineers build agents and LLM features into your product, every prompt, tool call, and RAG lookup becomes attack surface. Wingback secures that surface inline, without asking your team to rewrite the application.
The problem
An LLM feature takes untrusted input and turns it into actions: a database query, a tool call, an email, a payment. Prompt injection and jailbreaks aim to hijack those actions, and a RAG pipeline can be poisoned through the documents it retrieves. Traditional AppSec scans code, not the tool calls that code makes at runtime, so the interesting behaviour happens where it cannot see.
How Wingback secures AI applications
- One gateway, 12+ providers. Inline across OpenAI, Anthropic, Google, Mistral, Bedrock, Vertex, and more, with token-level policy on every prompt and completion. Deploy as a drop-in proxy, a Kubernetes sidecar, or an SDK guard, with no agent code changes.
- Prompt defense in four tiers. Signatures, embeddings, a Prompt Guard classifier, and an LLM judge catch injection and jailbreaks even when obfuscated.
- Data protection both directions. 25+ PII entity types plus secrets, with detect, mask, or block, so sensitive data does not enter or leave through the model.
- MCP gateway. Per-tenant credential isolation, tool input/output scanning, and a curated server catalog for the tools your agents call.
- Adaptive red teaming. Continuous adversarial testing that hardens the app and compiles findings into runtime guards.
FAQ
- What is AI application security?
- AI application security protects the LLM-powered features and agents an organization builds into its own products: the prompts, models, tool calls, RAG pipelines, and MCP servers behind them. It defends both the inputs (prompt injection, jailbreaks) and the actions (tool misuse, data exfiltration) at runtime.
- How does Wingback secure AI applications?
- Wingback sits inline as an AI gateway across your model providers and as an MCP gateway for your tools. It applies token-level policy on every prompt and completion, a four-tier prompt-defense pipeline, PII and secret detection in both directions, and per-agent guardrails. It deploys as a drop-in proxy, a Kubernetes sidecar, or an SDK guard, with no agent code changes.
- Does Wingback stop prompt injection in my app?
- Yes. A layered pipeline (signatures, embeddings, a Prompt Guard classifier, and an LLM judge) catches injection and jailbreaks even when obfuscated with base64 or homoglyphs, and blocks the downstream action the injection was trying to trigger.
- How do you red-team a RAG or agent application?
- An adaptive LLM-driven attacker tests your models, agents, and RAG pipelines across 800+ techniques, maps every finding to the OWASP LLM and Agentic Top 10 and MITRE ATLAS, and auto-compiles each validated finding into a runtime guard so the same attack cannot run twice.