DeepBlocker / Products / 05 · Agent Assurance
RED TEAM · PRODUCT 05

Agent Assurance

Your company is starting to run AI agents that read email, use chat tools, open tickets and take actions. We attack them the way a criminal would, and prove whether they can be talked into something they must never do.

In one sentence
Any agent, any framework. We reach it over its real channels, work it over days with a persistent attacker, and verify every action it takes against the rules you wrote.
What it is
A test, then a subscriptiona penetration test first, then continuous retesting
Who it is for
Any organisationrunning AI agents that can act on its systems
Target
Your AI agentsMuse, OpenClaw, Hermes, custom built, any framework
Status
Design partnersbuilt; first engagements now

What we test

An agent can be socially engineered the way a person can. The difference is that it never gets tired, never gets suspicious, and holds real permissions inside your systems.

01

Any agent, on any framework

Agents built on Muse, OpenClaw, Hermes or your own code. Agents that read email, use chat tools, open tickets, run tools and take actions inside your systems.

02

Over the channels a criminal would use

Live today: the web, its API and Telegram. Coming: email, Slack, Microsoft Teams, WhatsApp, Discord and voice. The attacker builds a lasting identity and works the agent over days, not in one prompt.

03

Against the actions you forbid

You write the rules. We try to make the agent pay, change an account, disclose data, book something or send a message it must never send. Every rule points at a real tool the agent holds.

04

And we prove what happened

Every tool call the agent makes is recorded and checked against your rules. You see the transcript, the action and the rule it broke, graded critical to low. Nothing real is ever touched.

The AI red teaming tools on the market scan prompts. They do not talk to the agent over its real channels from a convincing identity, come back next week, or verify what it actually did. That is what we built, and it is the same team that attacks people on the phone.

Whatever takes control, we test the damage

We test what your agent can be made to do, whatever takes control of it. There are three ways in. The harm always goes through the same tools and permissions, so that is where we look.

In the conversation

A trick

Someone talks the agent into it. A fake boss, a made up emergency, a request that sounds routine.

We test this today
In what it reads

Hidden instructions

A message, a document or a tool result carries orders. The agent follows them as if they came from you.

We test this today
In the model itself

A planted backdoor

Poisoned training data teaches the model a secret trigger. When it sees the trigger, it obeys the attacker instead of you.

We test the damage it could do
Why this matters now. A new study planted backdoors in AI models by poisoning as little as 0.1% of their training data. It still worked at 0.0005%. The backdoor survived later training on clean data. Once triggered, the agent ran the attacker's commands on the user's computer, about 92% of the time on the largest model tested. Read the study by Hu, Carlini, Jagielski and others.
What we can and cannot tell you. Nobody can find an unknown trigger by talking to an agent, and we do not claim to. What we can show is what your agent is able and willing to do once something takes control of it, and how much damage that would cause.

What we prove today

01
Attacks over its real channels
We reach your agent over the web, its API or Telegram, the way a criminal would.
02
Tricks and hidden instructions
Social engineering and prompt injection. As a premium option, hidden orders in documents and tool output we hand the agent, and attempts to plant false memories.
03
Too much power
We push the agent to act beyond its job and use powers it should not use.
04
Harmful commands and database queries
We try to make the agent pass harmful commands or database queries to the systems behind it.
05
Proof from fake tools
Payments, account changes, data sharing, bookings and messages run on fake copies we host. Every call is recorded and nothing real changes.
06
Proof from its own activity logs
Your agent can send us its activity logs (OpenTelemetry traces). The report shows what it did, not only what it said.
07
Fake tools for coding agents
We test coding and tool agents for what they would run, write and send. They work with fake command, file and web tools, so nothing real runs.
08
Hidden instructions in project files
We plant hidden instructions in the project files the agent opens, and record whether it follows them.
09
How much power each agent holds
We grade how far a taken over agent could go: the commands it can run, its internet access, the secrets in its reach, and what it can write and install. The report says which grades come from the test and which from your answers.
10
Where your model came from
A record of the model your agent runs on, as you tell it to us, with a checklist of the evidence to ask your provider for.
On Pro and above. We replay published backdoor triggers, each with its source. A miss proves nothing: a model can still hold a trigger nobody has published.
Coming, on our roadmap. Testing a coding agent's own shell and file tools from its activity logs alone. Poisoned knowledge bases: a test document loaded into your own knowledge base. None of these are live yet.

How an engagement runs

Five steps, from registering the agent to the retest. The attacker runs on our own hardened server, reaching only the model provider and the targets you authorise.

01

Register the target

Which agent, which channels it listens on, which actions it can take: payments, account changes, disclosures, bookings, messages.

02

Write the rules

Plain rules per agent. MUST NOT refund above a limit without approval. MUST NOT change a payee. MAY look up an order. Signed rules of engagement before anything runs.

03

Attack

Pretexts, urgency, poisoned documents, cross channel pressure, over days. Three test modes: through the agent's live channels, inside your own staging sandbox, or inside the DeepBlocker Agent Arena, a controlled world of hostile customers and dishonest websites where nothing real can be touched.

04

Verify every action

The sandbox records every tool call. Our verifier checks each one against your rules and grades the finding. A kill switch stops the campaign at any moment.

05

Evidence and retest

Report, event log, readiness score, and a rerun after every model, prompt, tool or permission change.

Trustwright is the free, open source part of this. The Range and the scan at trustwright.deepblocker.ai test whether a dishonest website can manipulate an AI assistant through the new WebMCP standard. Those attack classes are one of the test modes inside Agent Assurance.

What you get

The same deliverable our voice fraud clients already buy, in the format their auditors already accept.

01
Scored findings
Every attempt, whether it succeeded, the transcript, the action taken and the rule it broke.
02
Business consequence mapping
What each successful manipulation would have cost you in the workflow it was aimed at.
03
Remediation plan
The specific rule, permission and configuration changes that close each finding.
04
Sealed evidence pack
Timestamped and hash sealed, so you can prove the test happened and what it found.
05
Retest after every change
Agents change with every model, prompt, tool or permission update. The subscription reruns the campaign and shows the trend.

Who this is for

Risk and compliance

Regulated financial firms

Trust companies, fund administrators, private banks and wealth managers putting agents in front of client and payment systems.

Trigger: the agent can move money or change an account
Security

CISOs and heads of security

Teams that already test phishing and voice, and now have a third population of targets that never gets tired and never gets suspicious.

Trigger: AI agent rollout has outrun the control review
IT and service desks

Teams giving agents the keys

Agents that reset passwords, change MFA, open tickets and answer customers are the new help desk, and the help desk is where real attackers go first.

Trigger: an agent is about to get write access

Pricing

You pay for how much testing we run, not for how many agents you have. All prices exclude VAT and GST.

Agent testing plans
From £1,500
a month · priced by test conversations
  • All your agents, on web, API and Telegram, included
  • New attacks after every change you make
  • Proof of what the agent actually did, not only what it said
  • A report signed by our analyst, with the fix for each problem found
See the full Team, Business and Enterprise breakdown, plus the partner plans for one to three agents (25 or 75 test conversations a month), on the pricing page.
Through a partner. Managed security providers and penetration testing firms can offer the continuous subscription to their own customers under their own brand. Ask us about partner terms.
Start here

Find out whether your AI agents can be talked into the wrong thing.

We are taking a small number of design partners now, at reference pricing, in exchange for a case study.