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ExfilPath

About

ExfilPath Pvt. Ltd builds one thing, and builds it properly.

We are a Nepal-based engineering and research team working on application security for AI agent systems. Our team has spent years building AI systems for companies in Nepal and beyond, and ExfilPath grew out of seeing the same security gap across those systems: not weak components, but reachable routes between them.
Established August 2025Built by a Nepal-based teamApplication security for AI agent systemsProduct-led, research-driven

Mission

Make one dangerous question easy to answer.

Teams shipping agents are asked to reason about prompt injection, tool permissions, data boundaries and output handling all at once, usually with tooling that reports each in isolation. The result is a long list and no decision.

ExfilPath collapses that into a single question with an actionable answer: can attacker input reach an action that causes damage? If it can, here is the route and the smallest change that closes it. If it cannot, you are done.

We think that is the smallest useful unit of agent security, and that dashboards, scores and policies are decoration on top of it.

What we do

We build static analysis for AI agent systems. Given source code, ExfilPath produces the map of routes between untrusted input and damaging actions, with the fix for each open route.

What we do not do

We do not sell a dashboard for everything, we do not run your agents, and we do not ask for credentials. One question, answered thoroughly, is the whole product.

How we fund it

Through plans that teams choose after evaluating the platform, not through gated evaluations. That is why analysis is free in early access and priced transparently for general availability.

How we publish

Every detector traces back to a public disclosure or a pattern found in real agent code. The reasoning is on the research pages, with links to primary sources.

How we got here

Four steps, in order.

  1. The observation

    Agent code kept passing every scanner

    Reviewing agent systems, we kept reaching the same result: the tooling was green, and a careful reader of the code could still draw a line from an inbound message to an outbound request. The tools were answering component questions. The risk was a composition question.

  2. The research

    Public breaches confirmed the shape

    EchoLeak in Microsoft 365 Copilot and ForcedLeak in Salesforce Agentforce were not misconfigurations. Both were reachable routes through correctly configured systems. That is when we stopped writing warnings and started modelling paths.

  3. The platform

    One question, made machine-checkable

    ExfilPath encodes that reasoning as 46 detectors across input surfaces, sensitive actions, controls and model boundaries — and reports only what actually connects.

  4. Today

    Early access, in the open

    The platform is in the hands of engineers, the detector logic is documented publicly, and the research behind every rule is published rather than described. Plans are priced and visible so teams can plan ahead.

Team

Engineers and researchers, answerable for the engine.

Based in Nepal, the team brings years of experience building AI systems for companies in Nepal and beyond. The people who write the detectors are the people who answer questions about them.

Analysis engine

Interprocedural path tracing, language front-ends, and the precision work that keeps findings trustworthy.

Security research

Breaking agent frameworks, turning each break into detectors, and publishing the reasoning.

Product engineering

Making a path finding read like an explanation: traces, diffs, pipeline output and editor surfaces.

We are hiring across all three. Open roles.

Reviewed with security and platform teams in Nepal and beyond

NGNovaGrid SystemsAgent platform review
QLQuantumLeaf TechnologiesML pipeline review
VSVertexScale LabsBackend agent review
HNHyperNova CloudPlatform engineering review

Reviewers

Teams that pressure-tested the work with us.

These organisations reviewed the research, the detectors or the platform during development. Our early product feedback has been shaped especially by teams in Nepal, alongside teams beyond Nepal.
NG

NovaGrid Systems

Agent platform review

QL

QuantumLeaf Technologies

ML pipeline review

VS

VertexScale Labs

Backend agent review

HN

HyperNova Cloud

Platform engineering review

Early reviews

Engineers who put the platform against their own agents.

Our scanners were green while an email could still walk a summary straight out to an external host. ExfilPath was the first tool that drew that route instead of handing us another list.

EMEthan MercerPrincipal AI Engineer · NovaGrid Systems

The part that changed my mind was the trace through the model call. Everyone talks about prompt injection in the abstract; this showed the exact three lines that made it exploitable.

PSPriya ShahSenior Machine Learning Engineer · QuantumLeaf Technologies

It reads the repository, finds where untrusted text enters, and tells you which action it can reach. We closed two paths with one-line changes the same afternoon.

DKDaniel KimStaff Backend Engineer · VertexScale Labs

What I want from a security tool is a decision, not a dashboard. Open path or no open path is a decision my team can act on in a release meeting.

SASofia AlvarezDirector of Platform Engineering · HyperNova Cloud

How we build

Four rules we hold ourselves to.

One question, answered well

We are not building a dashboard for everything. We answer whether a route exists and how to close it.

Findings must be arguable

A finding you cannot verify is a finding you will ignore. Every path shows its trace, its controls and its reasoning.

Read code, never run it

Analysis stays static, so assessing an agent is never itself a risk.

Security tooling should be easy to adopt

No credentials, no agents to deploy, no data pipeline. Point it at code and get an answer.

Questions about the company or the platform?

Write to hello@exfilpath.tech and an engineer will answer.

Contact us