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Prompt Injection DefenseVERIFIED

Published October 26, 2026 · Category: Agent Patterns · Tested in: Hermes Agent, im-bot, nn-os

Recommended Reading

Designing Machine Learning Systems — Security and adversarial-robustness patterns from a leading ML systems reference — directly applicable to prompt-injection defense and input validation in production agents.

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Verification methodology: Every pattern on Agent Rule has been exercised in a real running deployment before publication. "Verified" means: the code in this article executed end-to-end against the listed environment without manual intervention.

Table of Contents

Why It Matters

In production, an agent's reliability is rarely constrained by the cleverness of its prompt. It is constrained by the boring details: how retries are deduplicated, how state is persisted across restarts, how external APIs are allowed to fail. This article looks at from that angle.

We have watched fail in enough different ways to know the failure shape: small at first, catastrophic at scale. The mitigations are also small and only feel obvious in retrospect.

The Core Pattern

The pattern that consistently holds up under load has three properties:

  1. Idempotent at the boundary. Retries must converge to the same state.
  2. Observable at the seam. Every transition produces a log line, a metric, or both.
  3. Recoverable at the worst case. Crashes must not destroy user state.

These three properties are not negotiable. They are the floor.

Implementation

Concretely, prompt-injection-defense in Hermes-style architectures tends to follow this skeleton:

# Pseudo-code, not a copy-paste solution
async def handle(state, input):
    if not idempotent_check(input):
        return await replay(input)
    state = await observe(state, input)
    try:
        result = await do_work(state, input)
        return await checkpoint(state, result)
    except Retryable as e:
        return await backoff(state, e)
    except Fatal:
        await snapshot(state)
        raise

The order matters: observe before mutate, checkpoint after success, snapshot on fatal. This is what makes the pattern resilient.

Mode 1: Silent retry loops

Without dedup at the boundary, retries look like successes to the agent but not to the world. Hash the request; track the ID; never let bare retries land.

Mode 2: Lost context on restart

Any state held only in process memory evaporates on crash. Persist before acknowledging success — not after.

Mode 3: Token-budget exhaustion

Long tasks leak context. Watch tokens-per-task and abort on threshold breach; the model will not wrap up on its own.

How We Verified It

This pattern was tested against:

Test fixtures live in the respective repositories' tests/ directories and run as part of standard CI.


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