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Agentic AI Security

Secure the AI agents you've given access to

Your agents read code, call APIs, query databases and run commands. We find out what they can reach, test how they fail, and build the controls that keep them in bounds. We run agents on production systems ourselves, under the same controls.

Scope an assessment → How we do it ourselves

The questions we answer

Six things you should know about every agent you run

  1. 01 Which agents, models and MCP servers are running, and who owns them?
  2. 02 What can each one reach, and with whose credentials?
  3. 03 What can it change without a person approving it?
  4. 04 Which inputs can steer it: documents, web pages, tickets, emails?
  5. 05 Is every action logged and traceable to a person?
  6. 06 How do you stop one that misbehaves?

Engagements

Assess, attack, then engineer

Inventory and threat model

We map your agents, models, MCP servers and tools, what each can access, and where untrusted input reaches them.

Agent and MCP assessment

We review tool permissions, credential handling, approval flows, logging and isolation against how the agent is actually used.

AI red teaming

We attack your LLM features and agents: prompt injection, data leakage, tool misuse and excessive agency, with a proof of concept for every finding.

Guardrails engineering

We implement least-privilege tool access, approval gates for risky actions, credential isolation, audit trails and prompt-injection guards.

Proof

We build agents that act on real systems, and the controls around them

OpsMaxx

AI agents operate real server fleets through 28 MCP tools, each set to allow, ask or deny. Agents never receive passwords, keys, hostnames or a root shell, output is redacted, and every action is audited.

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DSO

AI triages findings and writes fixes, and a person approves every verdict and every fix. The AI never receives the repository credential, and fixes over 400 lines are refused.

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X-Security

A prompt-injection guard for LLM endpoints, enforced at the API gateway and compiled deterministically, with no model in the enforcement path.

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Our research

In 2,700+ audited runs, changing only the scaffold around a model moved it from 0 to 49 solved pentest challenges. How an agent behaves depends on the system around the model, so that is where we test and put controls.

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What's included

Every engagement ends with controls, not just a report

  • Inventory and threat model of your agents, models and MCP tools
  • Agent and MCP security assessment
  • AI red teaming, including prompt injection and excessive agency
  • Least-privilege tool access, approval gates and audit trails
  • Prompt-injection guards at your API gateway

Testing a single LLM feature? See LLM Pentest and LLM Security.

Book a call

Book a 30-minute call

Pick a time that suits you. We'll talk through what you're building and where your security stands, then suggest where to start.

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Cyphlon

1:1 Consultation Call

30 min Google Meet

An introductory call with our engineers about any engagement: a pentest, continuous testing, managed AppSec, security engineering, agentic AI security or a fractional CISO. We'll ask about your stack, your deadlines and what your customers or auditors expect, and tell you plainly if we're not the right fit.

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About Us

Cyphlon is a security engineering company. We implement DevSecOps, application security, Zero Trust and supply-chain security, build the products we deploy, and publish our research.

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Company

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Services

DevSecOps Application Penetration Testing Supply Chain Security Zero Trust Architecture LLM Security

Products

Chainsaw DSO Acksess SHD Investigation Platform X-Security OpsMaxx LVCScan

Research

Backbone or Backbone-Architecture? A Controlled Study of LLM Agents on Web Penetration-Testing CTFs

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