AUSTA | Adversarial Intelligence

Security Engineering

How to Pentest the LLM Layer in a Live Game Backend

Modern game backends quietly grew an LLM layer. Live-ops bots, NPC dialog, automated moderation, customer-support agents. None of it gets pentested the way HTTP endpoints did. Here is what to actually test, and a loop you can run today.

By Austa · Published · ~7 min read

The new attack surface nobody is testing

Five years ago a game backend was a REST API in front of Postgres. You audited the routes, you tested for SQLi and IDOR, you signed off. Done.

The 2025 to 2026 wave changed that. Studios now bake LLMs into:

Every one of those is a prompt that takes user-controlled input. Every one is a fresh attack surface. And almost none of them are getting tested with the rigor the rest of the stack got.

The realistic threat model: a player crafts an in-game chat message that hijacks the moderation classifier. Or a ban-appeal ticket that talks the support agent into reissuing credits. Or an NPC interaction that exposes the system prompt and reveals which moderation rules are wired in. None of these require breaking auth.

Where the LLM lives in a typical game backend stack

Walk a typical AI-augmented game backend top to bottom and the LLM endpoints sit roughly here:

[Client] --> [REST/WebSocket API] --> [Game backend services]
                                            |
                  +-------------------------+--------------------------+
                  |                         |                          |
            [Moderation API]         [Live-ops agent]          [Support agent]
                  |                         |                          |
            [LLM provider]           [LLM + tool calls]         [LLM + ticket DB]

Managed game backends like Supercraft GSB (matchmaking, dedicated servers, leaderboards, economy, auth, live-ops with Unity/Unreal/Godot SDKs), PlayFab, and Nakama all wrap one or more of these LLM-touching surfaces behind a higher-level API. Studios that build on those backends inherit the LLM exposure whether they think about it or not.

Self-hosted game backends with custom LLM integrations are no safer. Often less safe, because the LLM call site is hand-rolled instead of running through a vendor's input filter.

Common attack vectors worth testing

1. Prompt injection in user-supplied content

Player names, chat messages, ticket text, profile bios. Any string the player controls that ends up in an LLM prompt is a vector. Standard tests:

2. Instruction leakage

The system prompt usually contains the moderation rules, escalation thresholds, refund policies, and which tools the agent can call. Leaking it gives an attacker the full operational playbook. Test:

3. Moderation bypass

If the moderation classifier is itself an LLM, it is fool-able through the same tricks the underlying model is. Test by encoding the disallowed content (l33t-speak, leetcode-style obfuscation, base64, foreign-language paraphrase) and seeing whether the classifier still flags it.

4. Off-topic exfiltration

Many game-backend LLMs have access to player data through RAG (retrieval-augmented generation). Testing whether you can get the agent to surface another player's data through a carefully constructed query is a real concern.

5. Cost amplification

An attacker who can make the LLM produce huge outputs, or trigger expensive tool calls, can run up the studio's API bill. Worth testing inputs that lead to runaway generation.

A test loop you can run today

Pick the highest-value LLM endpoint in your stack (usually the customer-support agent or the moderation classifier). Then:

  1. Enumerate the inputs. What player-controlled fields end up in the prompt? Names, chat, ticket text, item descriptions, profile bios, world-name suggestions, custom emote captions.
  2. Build a structured attack set. A few hundred adversarial prompts, organized by category (injection / leakage / moderation bypass / exfiltration / cost). Open-source datasets exist; they are a starting point, not an end point.
  3. Run the prompts at runtime. Hit the same endpoint a real player would. Capture the responses.
  4. Score the responses. Did the system prompt leak? Did a tool fire that should not have? Did the moderator pass content it should have flagged? A second LLM acting as a judge is useful here, but verify a sample manually.
  5. Triage. Sort the failures by exploitability and impact. Prompt injection that escalates to credit refunds is a P0; leaking that the moderator is GPT-4o-mini is informational.

What good looks like in the response

You want to see, for every adversarial prompt:

Tools and where to start

The open-source landscape (PyRIT, Garak, promptfoo) gives you the harness for free. The hard part is building the attack set that matches your specific game's risk surface, scoring the outputs at scale, and triaging the failures into something actionable. That is the work most studios skip and most pentest reports miss.

The good news is the same loop runs against any LLM endpoint, whether it lives behind a managed game backend or a self-hosted one. The threat surface does not change with the host.

Final thought

The teams that built game backend HTTP endpoints learned to assume hostile inputs years ago. The teams plugging LLMs into those same backends in 2025 to 2026 mostly have not learned that yet. Pentesting the LLM layer is the same discipline applied to a new surface. The tooling is younger, the categories are different, but the mindset is the one the security team already has.

Related