Security for Agent Connectors: least privilege, injection resistance, and safe toolchains

In March I called it the Compliance Cliff: the moment you stop demoing agents and start connecting them to real systems is the moment governance becomes a survival skill.

In April we saw agent runtimes emerge — SDKs, orchestration primitives, traces.

In May we hit the 1M-token era — and learned that context is now a budget and an attack surface.

In June and July we treated agents like distributed systems and multi-agent org charts.

So August is where the rubber meets the road:

Tools. Connectors. Credentials. Side effects.

Because the ugly truth is:

You can have a “safe” model and still ship an unsafe system…
if your connectors behave like a root shell with a friendly chat UI.

This article is a practical security playbook for agent connectors — focused on controls that teams can actually ship.

The Connector Threat Model (the one people skip)

Traditional apps have clear trust boundaries:

• user input is untrusted
• backend is trusted
• DB is trusted
• privileged credentials live server-side

Agentic apps blur those boundaries because the model:

• consumes untrusted content (web pages, emails, PDFs, tickets, chat)
• produces actions (tool calls) that cause side effects
• is vulnerable to manipulation (prompt injection, jailbreaking, data exfiltration)
• is non-deterministic (the same input can behave differently)

So the right mental model is not “LLM security.”

It’s:

Your agent is an untrusted process that can ask for power.
Your job is to make sure it can’t get more power than it should.

The three attacker goals

If you secure for those four, you’re already ahead of most teams.

Prohibited Practices (the “we’ll fix it later” cliff)

These are not theoretical. They are what breaks in the first incident review.

Those practices tend to appear because teams think of connectors as “integrations.”

But connectors are privileged execution surfaces.

So let’s build them like we build payment flows: carefully.

The Connector Boundary Pattern (the control that actually ships)

If you only take one architectural idea from this month, take this one:

Connectors do not live inside the agent.
They live behind a boundary that enforces identity, policy, validation, and logging.

I like to name that boundary the Connector Gateway (or Tool Proxy).

It has four jobs:

1. Auth brokerage: mint short-lived, scoped credentials per run / per tenant / per user
2. Policy enforcement: allow/deny + require approvals based on risk and context
3. Input validation: schema validation, type checks, safe defaults, and hard limits
4. Audit + telemetry: immutable logs, traces, and security signals

Least Privilege for Tools (practical, not aspirational)

Least privilege is easy to say and hard to ship because it forces you to answer:

• whose identity is this tool acting as?
• which tenant, project, or workspace?
• for how long?
• with what scope?
• with what rate limits and side-effect budget?

So treat least privilege as a token-minting problem.

Rule 1: Every tool call must have an identity

Not “the service.”

A real principal:

• user:<id> for user-initiated runs
• system:<workflow> for scheduled runs
• agent:<bot> for internal assistants

And that principal must map to a policy.

Rule 2: Make scopes boring and composable

Instead of crm:admin, create scopes like:

• crm:contacts.read
• crm:contacts.write
• crm:deals.read
• crm:deals.write

Then make “write” scopes require more friction.

Rule 3: Prefer ephemeral credentials

• short TTL
• refreshable
• revocable
• constrained to tenant + principal

If your agent uses long-lived credentials, you’ve already lost the benefit of fine-grained controls.

Injection Resistance (treat content as hostile)

Prompt injection isn’t magic.

It’s the oldest attack in software:

“Make a privileged system do something it shouldn’t… by feeding it convincing text.”

In agent systems the attacker doesn’t need code execution. They need instruction execution.

So the key design move is:

Separate “content” from “control”

• Content is untrusted: emails, pages, docs, tool outputs
• Control is trusted: system prompts, policies, tool schemas, allowlists

If you let content write control, you’re building a remote-control backdoor.

The connector-side rules that matter

1. The model can propose; the gateway disposes
2. Tool inputs must be structured and validated
3. Tool outputs must be treated as untrusted input
4. Side effects require friction proportional to risk

Here’s what “structured” really means: your tool interface should make unsafe requests impossible.

{
  "tool": "send_email",
  "args": {
    "to": ["customer@example.com"],
    "subject": "Your quote",
    "body_markdown": "...",
    "attachments": [
      { "file_id": "doc_123", "name": "quote.pdf" }
    ]
  }
}

Not:

Send an email to the customer with the attached quote and ask them to wire money to this new account.

The difference is everything: structured inputs give you enforcement hooks.

Safe Toolchains (side effects without fear)

A secure connector isn’t just “auth + allowlist.”

It’s a toolchain that makes side effects:

• idempotent
• rate limited
• reviewable
• reversible when possible
• auditable always

This is where June’s “eventually correct” thinking comes back:

Side effects need a transaction model

For write tools, I strongly recommend a two-phase pattern:

1. Plan: the agent proposes an action (what + why)
2. Commit: the system executes it with policy checks + optional approval

That can look like:

• staged drafts (email drafts, Jira drafts, PR drafts)
• dry-runs for infrastructure changes
• “preview diff” for DB updates
• hold-to-confirm for payments/refunds

A simple risk ladder that works

• Read-only → no human required, minimal friction
• Write-draft (creates a draft artifact) → allowed with logging
• Write-commit (irreversible or costly) → requires approval / second factor / workflow gate

Observability: security you can debug

Security controls that can’t be observed don’t exist.

Your connector boundary should emit a complete forensic trail:

• tool name + version
• principal identity + tenant
• request args (redacted as needed)
• policy decision (allow/deny/require-approval)
• latency + retries
• outcome (success/failure) + error class
• correlation IDs linking: prompt → tool call → downstream request → state change

I like to treat tool calls as first-class spans in tracing:

And then you add detection on top:

• spikes in denied calls
• repeated attempts to access write tools
• abnormal destinations (new domains, new recipients)
• unexpected data volume (exfil attempt signal)
• tool-call loops (agent stuck, or being manipulated)

A Build Plan You Can Ship in a Quarter

This is a “doable” implementation sequence that produces real risk reduction early.

Implementation Notes (what teams usually miss)

1) “Tool allowlisting” is not enough

Allowlisting tool names helps. But the real danger is arguments.

You must validate arguments, enforce limits, and apply policy to the intent of the call.

2) Your gateway needs an egress policy

If your agent can browse the web or call arbitrary URLs, you need:

• DNS/domain allowlists where appropriate
• outbound proxying
• logging and limits on external calls

Otherwise your agent becomes a data exfiltration machine.

3) Store “what the agent saw” for investigations

If you can’t reproduce:

• the prompts,
• the retrieved context,
• the tool outputs,
• and the policy decisions…

…you can’t prove what happened.

(And you can’t fix it confidently.)

4) The model is not your policy engine

Models can help propose actions. They cannot be trusted to enforce policy.

Policy enforcement must happen outside the model — deterministically.

Resources

FAQ

What’s Next

August was about securing the hands of the agent — the connector layer where side effects happen.

Next month, the lens widens to governance timelines and obligations:

GPAI Obligations Begin: what changes for model providers and enterprises

Because once your connectors are safe, the next question becomes:

What do you need to prove — and to whom — when regulators, customers, and auditors start asking?