securityoperationsrisk-management

How to Run a Red Team Against LLM-Powered Desktop Agents in a Quantum Lab

UUnknown

2026-02-10

10 min read

Step-by-step red team plan to assess LLM desktop agent risks in quantum labs—scoping, attack scenarios, IR, and mitigations.

Hook: Why your quantum lab needs a red team for Desktop agents for LLM-powered desktop agents—now

Desktop agents powered by large language models (LLMs) are moving from curiosity to core tooling in 2026. They automate experiment notes, synthesize data, and in some previews have direct file-system access and plugin ecosystems that can call external services. For technology professionals running quantum labs, that convenience introduces new, concrete risks: inadvertent data exfiltration, stolen API keys, corrupted calibration sequences, and — in worst cases — physical harm to hardware through malformed instrument commands. If your team is still asking "what could go wrong?", you need a structured red team exercise targeted at these LLM-led threat surfaces.

Executive summary — what this plan delivers

This step-by-step red team exercise plan is tailored for enterprise quantum labs and covers scoping, threat modeling, attack scenarios, telemetry checks, incident response adaptations, and remediation prioritization. It assumes agent orchestration frameworks may have access to developer tools, file systems, and instrument control APIs (a configuration increasingly common after late-2025 previews of production desktop agents). By the end of the exercise you'll have actionable findings: detectable attack paths, controls tested, and prioritized fixes to harden your lab against LLM-specific threats.

Context: Why 2026 matters for LLM desktop agents and labs

Late 2025 and early 2026 saw an acceleration in desktop AI agents that act autonomously: file-system-aware assistants, plugin ecosystems, and agent orchestration frameworks that can chain prompts and call local or cloud APIs. Many vendor announcements and FedRAMP-enabled AI offerings have increased enterprise adoption, including in research and government contexts. This shift means your lab's endpoints are now potential vectors into sensitive control systems and datasets — especially in quantum labs where instrument calibration and device integrity are high-value assets.

Key 2026 trends to keep in mind

Autonomous agents on endpoints: Agents performing multi-step tasks with local file and network access.
Plugin ecosystems: Third-party extensions that widen an agent's capabilities and attack surface.
FedRAMP and enterprise uptake: Increased adoption in regulated labs and government-funded facilities.
Regulatory focus and supply-chain scrutiny: New guidance on AI usage in critical environments.

Topline risks for quantum labs from LLM-powered desktop agents

Credential and secret leakage: Agents storing or sending API keys, SSH credentials, or tokenized instrument credentials to external services.
Prompt injection and jailbreaks: Malicious content or adversarial prompts tricking the agent into revealing secrets or executing disallowed actions.
Instrument misuse: Agents triggering calibration sequences, high-power pulses, or improper cooling procedures that could damage hardware.
Data exfiltration: Proprietary calibration curves, noise models, and raw measurement data sent outside the lab.
Supply-chain compromise via plugins: Malicious or vulnerable plugins executing arbitrary code under agent privileges.
Reduced forensic visibility: Agent abstractions may hide fine-grained user actions, making investigations harder.

Red team exercise overview: phases and objectives

The exercise follows a standard adversary lifecycle but with LLM-specific test cases. Use a controlled lab sandbox when possible. Objective categories map to business impact: data confidentiality, instrument integrity, and operational availability.

Planning & scoping
Threat modeling & scenario design
Execution (recon → access → escalation → objectives)
Detection & telemetry validation
Containment & incident response validation
Reporting & remediation

Phase 1 — Planning & scoping (practical checklist)

Good exercises start with strict rules of engagement (RoE) and defined boundaries. In quantum environments, safety overrides all other goals.

Define lab systems in scope: desktop OS versions, hostnames, instrument control endpoints (AWG, pulse schedulers), data pipelines (QCoDeS/Labber, data lakes).
Identify agent families in use (vendor name, version, plugins enabled).
Clarify physical risks and safety constraints — no actions that can irreversibly damage hardware.
Establish monitoring observers: blue team, instrument techs, safety officer.
Time-box the exercise and get pre-authorization signatures from lab leadership.

Phase 2 — Threat modeling & scenario design

Build threat models focused on how LLM agents change attacker reach. Use STRIDE augmented with LLM vectors: prompt injection and plugin supply chain.

Sample high-priority scenarios

Scenario A — Prompt injection for credential exfiltration: An adversary sends a crafted file or email that triggers an agent to include local credentials in a generated summary that is uploaded to a cloud drive.
Scenario B — Malicious plugin for command execution: A compromised plugin with access to instrument control libraries sends unsafe commands to an AWG.
Scenario C — Chain-of-prompts escalation: The agent uses its plugin to call a local script, which uses stored SSH keys to access a control VM.
Scenario D — Data poisoning and integrity attack: The agent modifies calibration files or merges corrupted calibration data into the pipeline causing silent drift.

Phase 3 — Execution: tactical playbook

Execute in a staged manner, safe-failing before attempting anything that could physically affect hardware. Each step must have detection expectations attached.

Reconnaissance

Enumerate agent capabilities: which plugins, API endpoints, and local file access are available.
Map user workflows where agents are used: experiment setup, data analysis, note-taking.
Collect telemetry sources: OS audit logs, agent logs, network flows, instrument logs. Consider feeding key metrics into operational dashboards for fast triage.

Initial access

Use simulated phishing or malicious files to test prompt injection. The goal is to get the agent to perform an action it shouldn't.

Deliver a benign-looking lab notebook or data file that includes embedded instructions specially formatted to exploit agent parsing.
Observe whether the agent rises above content filters and accesses local secrets (gitconfig, ~/.aws/credentials, instrument API token stores).

Privilege escalation & lateral movement

Test whether exported agent actions can be used to escalate. Example: agent writes a helper script that is then executed by a scheduled task.

Actions on objectives

Attempt the objective in a safe way first: instead of actually sending data out, simulate an upload (write to a quarantined sink controlled by the red team) or create a mock command that logs the would-be action.

LLM-specific TTPs and test cases

Prompt-injection chain: test nested prompts (document → agent → plugin) to see if content filters are bypassed.
Plugin sandbox escape: test whether plugins run with system privileges or are properly isolated.
Context-window leakage: feed secrets into agent context and attempt to get them reflected in outputs.
Credential replay: test if agent logs or error reports include sensitive tokens.
Agent orchestration abuse: run multi-step tasks where the agent calls external APIs to fetch malicious instructions.

Practical detection tests and SIEM rules

Design detection rules that capture the unique behaviors of LLM agents: frequent long-form outbound API calls, unusual file reads of credential stores, and child processes spawned by agent binaries.

Example Splunk/EQL-style detection (conceptual)

index=processes (process_name="agent.exe" OR process_name="agent") | transaction host maxspan=1m | search (event_count>10 AND dest_host IN (llm_api_hosts))

Or for Elastic / EQL:

process where process.name: "agent" and process.args: "--plugin" and network.direction: out

Quick PowerShell host check

Get-Process -Name 'agent*' | Select-Object Id, ProcessName, Path; Get-NetTCPConnection -OwningProcess (Get-Process -Name 'agent*').Id

Use these to detect unexpected agent activity and map network destinations to known LLM endpoints. Consider augmenting rules with predictive AI detection for behavior-based alerts.

Phase 4 — Incident response validation

LLM-induced incidents require augmented playbooks. Your IR plan must answer: how do we safely stop an agent without losing forensic evidence or leaving instruments in unsafe states?

Containment steps

Isolate the endpoint from the lab network (maintain connectivity to control team channels as needed).
Quarantine agent processes (suspend, then collect a memory dump before termination).
Revoke affected API keys and rotate secrets used by instrument controllers; move credentials into a secrets manager or vault where possible.
Lock or pause instrument control VMs and orchestration services to prevent automated retries.

Forensics checklist

Agent local logs and plugin logs (timestamps, executed plugin flows).
OS process trees and memory dumps.
Network captures (pcap) focusing on egress to cloud LLM endpoints or unknown hosts.
Instrument control logs (command queues, state transitions), and calibration files.
Any file artifacts the agent wrote or modified.

Phase 5 — Post-exercise reporting and remediation

Your report should map findings to business impact and provide prioritized mitigations. Use a 1-2-3 remediation model: Immediate (high), Short-term (medium), Long-term (low).

Immediate (High) fixes

Block agent egress to unapproved LLM endpoints and add allowlists for required services.
Rotate exposed credentials and revoke plugin tokens.
Disable risky plugins and remove agent access to instrument control credentials.

Short-term (Medium) fixes

Implement file-read controls that prevent agents from accessing credential stores (use OS ACLs and secrets managers).
Harden plugin signing policies and require enterprise-managed plugin registries.
Improve telemetry: process lineage, agent-specific audit logs, and network metadata for LLM endpoints.

Long-term (Low) fixes

Introduce instrument command gateways that mediate commands and enforce safety policies.
Adopt ephemeral workstations or containerized experiment sessions for running agents with least privilege.
Establish a vendor review program for AI agents and plugins.

Measuring success: metrics & KPIs

Time-to-detect (TTD): From agent action to alert.
Time-to-contain (TTC): From detection to isolation of host.
Telemetry coverage: Percent of agent activities correlated with logs/pcap entries.
Attack surface reduction: Number of agent privileges removed in the remediation plan.

Operational controls and architectural patterns that work

Below are practical controls that balance usability and safety in research labs.

Network & egress controls

Egress filtering: allow only vetted LLM endpoints and block free-form uploads to unknown cloud storage.
Use proxy gateways that insert integrity checks (strip file attachments containing secrets).

Secrets & credential hygiene

Move instrument credentials into a secrets manager with short TTLs; agents must request short-lived tokens via an approval service.
Audit token issuance for user and agent requests separately.

Host hardening

Application allowlisting for instrument control binaries; sandbox agents in constrained containers when possible.
Disable agent write access to critical directories (calibration, firmware repositories).

Instrument control gateways

Introduce a mediation layer that requires signed commands and applies pre-defined safety guards (rate limits, parameter validation). This is the most effective way to prevent an agent from issuing unsafe low-level instructions to devices.

Case example (red team vignette)

In a controlled exercise in late 2025, a red team used a malicious notebook attachment to trick a desktop agent into summarizing a folder containing a plaintext instrument API token. The agent attempted to upload the summary to a cloud drive. The lab's proxy blocked the egress, generated an alert, and the blue team identified the token exposure path. Remediations included agent configuration changes, proxy allowlist tightening, and moving tokens into a vault — all validated in a follow-up test.

"Treat LLM agents like any other privileged user — but with added attention to content parsing and plugin behaviors."

Checklist: Ready-to-run red team playbook (condensed)

Get authorization and document RoE.
Inventory agents, plugins, and instrument endpoints.
Design 3–5 attack scenarios with safety gates.
Prepare detection queries and record expected telemetry.
Execute recon and initial access in sandbox; escalate only with safety approval.
Collect forensic artifacts and trigger IR playbook.
Deliver findings and remediation roadmap; schedule re-test.

Final recommendations — balance innovation and safety

LLM-powered desktop agents are powerful productivity boosters for quantum researchers, but they also reframe risk boundaries. Your lab must adopt a layered approach: strict endpoint controls, robust telemetry, secure secrets management, and instrument mediation. Red team exercises focused on LLM vectors will reveal brittle assumptions before adversaries do.

Call to action

Start with a scoped tabletop within your lab this quarter. If you want a ready-to-run template, download the qbit365 LLM-Agent Red Team Checklist (includes detection queries, RoE templates, and remediation playbooks) or schedule a workshop with our red team/quantum-opsec practitioners. Protect your experiments and IP before the next autonomous agent lands on a scientist's desktop.

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