Every security vendor now ships an "AI agent". Most of them are a chat box with a system prompt. An actual agent is a loop: it plans, chooses a tool and its arguments, runs it, reads the result, and decides what to do next — repeating until the work is done or it can prove it cannot proceed. That loop is genuinely useful in security work, and it is also a reliable way to cause damage quickly. Both halves deserve equal attention.
What the loop actually looks like
- Plan. Break the request into steps and name the capability each step needs — reconnaissance, service enumeration, content discovery, vulnerability matching.
- Select. Choose a concrete tool from a registry and build its arguments. This is where most implementations quietly fail: a model asked for
nmapwill happily producenmap(url=…)instead ofnmap(target=…, ports=…). - Execute. Run it under real constraints: timeout, concurrency slot, scope check, output capture.
- Read. Parse the result as structured data. "The scan finished" is not a result; "four hosts, eleven open ports, two version strings" is.
- Decide. Continue, adjust, or stop. A loop with no stop condition is not autonomy, it is a runaway bill.
Where agents are already better than a human with a terminal
- Breadth-first enumeration. Running eight tools across a large scope and keeping track of which host got which scan is tedious and error-prone. Machines are good at tedious.
- Triage and correlation. Reading four hundred output lines and grouping them by service is exactly the task that benefits from summarisation, provided the underlying data is real.
- Coverage bookkeeping. An agent can hold a ledger: reconnaissance done, enumeration done, vulnerability matching not yet run. Humans lose that thread after the third console tab.
- Repetition across similar targets. The same pipeline applied to twelve hosts, with consistent parameters and consistent output naming.
Where they still need a human
- Novel exploitation. Chaining an unusual access-control flaw into privilege escalation requires a model of how the application was built. Current agents pattern-match.
- Business-logic bugs. "You can buy the item for a negative quantity" has no signature to match.
- Impact judgement. Whether a finding matters depends on the business, the data and the contract. That is a decision, not an inference.
- Anything destructive or third-party. Writing, deleting, emailing, or touching a system outside the agreed scope.
An agent is a competent junior operator with unlimited patience and no judgement. Give it the repetitive work and verify the output. Do not give it the decisions.
The failure modes worth testing before you trust one
These are the ones we hit while building ours, and they are not exotic — they are the default behaviour of a loop built on a language model without guardrails.
| Failure | What it looks like | What stops it |
|---|---|---|
| Hallucinated tools | The agent "opens" /tools/crashpad or calls a tool that does not exist |
Validate every tool name against the live registry; return a hard error the model can see |
| Wrong argument shape | A URL is passed to a field that expects a hostname, or "4" where an integer is required |
Schema-driven argument building plus type coercion before dispatch |
| Loops | The same scan runs four times because the agent forgot it had already run it | A mission ledger keyed on tool + target + mode, with duplicate calls rejected |
| Fabricated results | A "finding" appears with no corresponding tool output | Only report parsed tool output; never let the model summarise from memory |
| Self-scan | The platform is asked to scan its own API or localhost | Target-shape validation and an SSRF/self-scan guard before any slot is taken |
| Unbounded spend | Twelve parallel scans against a wide CIDR | Per-plan concurrency caps, timeouts, and a kill switch per run |
| Silent stop | The run ends after one step with no explanation | Coverage checks: if a required capability was never exercised, say so explicitly |
Guardrails that belong in the platform, not the prompt
A prompt is not a security control. Anything you tell the model in natural language can be talked around by a sufficiently creative request, so the enforcement has to live in code the model cannot rewrite:
- A registry allowlist. If the name is not in the registry, it does not execute.
- Target validation before dispatch. Reject loopback, private ranges, link-local and the platform's own addresses — and re-check after DNS resolution, not just on the literal string.
- Per-user filesystem isolation. Output paths derived from the authenticated identity, never from model-supplied strings.
- Postcondition checks. "Exit code zero" is not success. A scanner that exits cleanly having matched nothing on a target that should have matched something is a failure worth surfacing.
- An audit trail. Every tool call with its parameters, its operator (human or agent) and its result. This is what makes an agent defensible in review.
How to evaluate an autonomous security agent in an afternoon
- Give it a task with an obviously wrong target —
127.0.0.1, or a private range. It must refuse with a clear reason. - Ask for two tools in one operation and check that both actually ran, not just one and a summary.
- Ask for the command that ran. If it cannot produce it, nothing else in the demo matters.
- Give the same task twice in one session and see whether it repeats the work.
- Stop a run mid-scan and confirm the process dies.
- Ask a question about a host that does not exist and confirm you get an error, not a finding.
An agent that passes all six is worth trialling. An agent that fails any one of them will eventually put something untrue in a report you signed.
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