You don't need a security background to read this. We'll define every term as we go, and end with a plain-language glossary. In short: red-teaming means safely attacking your own AI on purpose, in an agreed test environment, to find where it can be tricked before a real adversary does.
A chatbot only talks. An AI agent acts: it browses the web, calls APIs, reads files, queries databases, sends email, and takes multi-step actions on a user's behalf. Give a model tools and you've given it hands, often the same access your staff have, sometimes with standing credentials that never expire.
Plain analogy: a chatbot is a receptionist who answers questions; an agent is an employee with keys to the building. As opposed to an inaccurate output from a chatbot, an agent can get a wrong refund approved, a customer record emailed out, a database query run against a password column. Most organizations are still building the inventory, controls, and operating processes needed to govern AI agents consistently, even as those agents are handed real credentials.
Modern aligned models pass the obvious test: ask for something clearly harmful in one line and they refuse. That's exactly why single-prompt testing gives false comfort. Two blind spots survive.
① Multi-turn pressure. A goal refused head-on gets approved once it's patiently broken into innocent-looking steps.
② Instruction-hierarchy confusion. The model treats data it merely reads — a retrieved document, a tool's output — as instructions it should obey.
A coding agent refuses to open a secrets file — but complies when asked to "summarize what's in /etc/ for the audit."
A support agent refuses a refund beyond policy — but approves it after a user patiently decomposes the request across 8 turns.
A document-lookup assistant (a "RAG" system) refuses to quote its hidden setup instructions — but echoes them after being told to "cite all sources verbatim for compliance."
None of these show up in a single prompt. The failure lives in the conversation and in the untrusted data flowing through the agent, so the test has to live there too.
Red-teaming involves deliberately testing your own AI system to identify vulnerabilities before an attacker can exploit them. Think of it as a fire drill for AI. The goal isn't simply to find flaws, but to understand why they occur, how to fix them, and how to document the results for security and compliance reviews.
Fires a fixed list and never sustains the multi-turn pressure that actually breaks agents.
Responds to the model's last answer and pivots after a refusal — the way a real attacker does.
AI agents respond and adjust to each interaction, so their vulnerabilities can change as a conversation progresses. Static checklists often miss these kinds of failures.
Many red-team workflows rely heavily on an LLM judge (a language model scoring its own work) and report "100% defended" — while the agent was quietly leaking in its tool calls. A scanner that only reads the final chat message misses the breach happening in the tool log: an outbound email, a database write, an API request the user never sees.
Fisher's answer: score the things that matter with no opinion in the loop. We plant a unique, harmless marker where a real secret would sit, then watch for that exact marker to appear verbatim in the agent's actions. If it shows up, the payload provably escaped. No LLM judgment required. That's why our proof is judge-free for the highest-stakes cases: data exfiltration (a secret leaving through an action) and prompt injection arriving through trusted channels (the hidden-instruction trick from earlier).
Here's what makes Fisher different — the what and why, in plain terms.
Attack strategies breed and mutate across thousands of episodes (each a full attack conversation), learning from partial progress instead of firing a static list.
Each strategy becomes contextual follow-ups that respond to what the model actually said, and pivots after a refusal.
Every finding is replayed several ways and ranked from deterministic under the observed replay conditions down to anecdotal (a lucky one-off we set aside).
For exfiltration and injection through trusted channels, proof comes from the planted marker — not an LLM's opinion.
Each run distills into a private library, so the next audit starts smarter and helps prioritize a new model's likely weak spots before testing begins.
Works with the popular toolkits teams build agents on (LangChain, LangGraph, MCP tool servers) and any standard web API, so we test your real agent, not a model in a lab.
Each finding includes the evidence needed to reproduce and investigate the vulnerability, rather than just a score on a dashboard.
Audit and governance teams can review the original conversation, the agent’s actions, any changes to system state, the confidence and severity of the finding, and the specific policy or control violated. They also receive the steps needed to reproduce the issue in an approved test environment.
Fisher supports continuous security testing throughout AI development and deployment, helping teams assess risk at key decision points using a consistent testing process and reproducible evidence.
Run an Assessment as your launch gate. Fisher pressure-tests the release candidate in a sandbox and returns a ranked list of exploitable paths, each with a replayable bundle and a 0–10 risk score. You make go / no-go on evidence, and the findings become your documented sign-off record.
Agents drift: a new base model, a tweaked system prompt, or one new tool can quietly reopen a hole you closed. Retest after meaningful changes to the model, prompt, or tools, before the release ships, and check that an earlier fix still holds.
Evaluating a third-party agent? You rarely get its source code. Fisher only needs to talk to it through its API or MCP endpoint to probe it like a real adversary. Get an independent, framework-mapped risk picture before it touches your data, and re-run at renewal.
Auditors want proof, not adjectives. Every finding is tagged to the frameworks you report against and ships as a bundle with the artifacts needed to replay it, so triage and audit prep come from one artifact.
When someone reports "the agent did something it shouldn't have," Fisher reproduces the behavior where possible, labels how reliably it fires, and — once you patch — proves on an isolated replica that the exploit is actually closed.
Turn thousands of adversarial episodes into a single defensible risk story: what can go wrong, how likely, how severe, and which obligations it touches, without exposing raw attack content or any production data.
You don't hand us your keys. Fisher can run against an externally-simulated copy of your agent, wired to synthetic data and planted markers. No production access, no real secrets, no source code, and no live customer data are required to get a real risk picture.
The synthetic environment behaves like the real one, so findings transfer, but a leak in the sandbox spills only fake data and fake markers. When Fisher proposes a fix, it proves that fix on an isolated replica and grades it honestly. Your production stays untouched the entire time.
Fisher findings can be mapped to relevant security and compliance standards, allowing teams to use the same evidence for technical investigations and audits. Engineers can reproduce and address vulnerabilities, while auditors can identify the controls affected by each finding.
Names are referenced for mapping only; no endorsement or certification is implied.
We point Fisher at the agents in scope and hand back a reproducible evidence package designed for security, governance, and audit review.
The roadmap:
Find and prove the exploitable paths, each with replayable evidence.
A config-level fix, proven on an isolated replica — production untouched.
Deep Model Trust — agents with scoped, short-lived authority.
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