llm security
Deterministic Agent Controls
Runtime enforcement for LLMs that stops prompt injection, data exfiltration, and agent misuse before execution.
Impart sees what your model sees, before and after. Sanitize what comes in, validate what goes out, and keep sensitive data from leaving the building.
LLM protection at runtime that enforces, not just observes.
Most LLM security tools analyze outputs or scan inputs in isolation. That’s not how attacks work. Prompt attacks are attempts to drive behavior.
Evaluate intent at runtime to control what reaches your model, not just what comes back.
Inspect
Every prompt in full context. Session history, identity, prior model interactions, and the chain of tool calls behind it.
Decide
What the prompt is actually trying to do. Override system instructions, exfiltrate data, manipulate downstream services. Intent gets classified, not just content.
Enforce
Malicious intent stops before it reaches the model. The decision is made inline, in milliseconds. No second-guessing required.
Sequence-aware LLM enforcement.
AI attacks rarely happen with a single prompt. Impart’s LLM protection enforces the full sequence on the same ingress-layer enforcement engine that inspects your full attack surface:
Discover
Detect
Protect
Govern
Discover
Every LLM making requests through the enforcement layer, whether sanctioned or not, to establish full visibility across your attack surface.
Detect
Malicious LLM attacks by evaluating the full action sequence, instead of just single anomalous requests, to spot patterns like privilege escalation or data exfiltration.
Protect
Against abuse and exfiltration by classifying the LLM’s intent and blocking or modifying malicious behavior in milliseconds, before the action executes.
Govern
And enforce LLM behavior using policy-as-code rules, which are written against live traffic and continuously refined to guide safer operation and stop unauthorized tool use.
One runtime engine for all your models.
Inline
Sits between the caller and the model — enforcement fires before the model processes the input
Stateful
Full interaction history maintained across the session — intent accumulates, enforcement tracks it
Unified
Whatever path the agent takes through your stack, the runtime knows who it is.
What a blocked attack looks like in Impart.
A coordinated attack unfolds in phases. Impart sees it as one sequence and stops it in real time.
Recon
The entity is identified and fingerprinted at first contact.
Behavioral context begins building immediately across every surface.
Auth Probing
Suspicious auth behavior emerges. Velocity and access patterns signal risk tracked across the entire system in real time.
Extraction Attempt
Intent becomes clear. Sensitive data access and outbound activity are evaluated against full behavioral history.
Enforcement
A rule is generated from the observed behavior, blocking the attacker going forward. What started as a sequence becomes a denial.
Running in production. Enforcing in real time.
"The Impart team is really innovating in the API security space. Really smart use of LLMs in their product that help security teams especially with firewall rules, which are a huge problem."
Travis McPeak, CEO
"API security is now a critical aspect of every application security program. Every CISO needs to have an integrated solution that can comprehensively protect their APIs across their entire lifecycle."
Zane Lackey, Co-Founder
"Great product. Great team. Makes application security so much easier and installs in minutes across both legacy and modern tech stacks."
Steve Hopkins, CTO
"When we think about examples of customer love in cybersecurity, some of the most loved companies in security includes Impart Security."
Ross Haleliuk, Head of Product
"Hands down one of the best API security products on the market and the most compelling solution for serverless. Integrates with no architecture impact, and great team to work with."
Miguel Calles, Engineer
"Examples like Thinkst Canary, Duo Security, Tines, Chainguard, Material, Impart, Panther, Anvilogic, and LimaCharlie show that it is possible to be pragmatic (and successful!) as a business and loved at the same time."
Rami McCarthy, Security-at-Large Leader
"The team is building something truly top notch in WAF, API Security, and LLM Protection."
Phillip Maddux, CEO
FAQ
Where does Impart sit relative to the model?
Inline, between the caller and the model. Enforcement runs before the prompt reaches inference, so policy decisions happen pre-execution, not post-response.
What happens when a prompt is blocked? Do users see an error?
You decide. Allow, block, or modify are all valid actions. Many teams modify (sanitize the prompt, strip the override attempt, return a controlled response) rather than block outright. The behavior is configurable per rule.
How does this work with agents that call tools?
Enforcement fires before tool execution. If a prompt is trying to drive an agent toward an unauthorized action, Impart stops it before the tool is called, not after the call has already touched a downstream system.
What models and frameworks does Impart support?
Impart is model-agnostic. Enforcement happens at the boundary, not inside the model. OpenAI, Anthropic, self-hosted, fine-tuned, and custom models all work the same way. Same for orchestration frameworks.