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AI Desktop Agents: Threat Models and Mitigations for Access to Local Files and Processes

UUnknown

2026-02-19

11 min read

Practical threat model and mitigations for desktop AI agents (Cowork). Learn detection signals, policy patterns, and one-click remediation to cut MTTR.

AI Desktop Agents: Threat Models and Mitigations for Access to Local Files and Processes

Hook: In 2026, enterprises are racing to enable productivity with desktop AI agents like Anthropic's Cowork—but giving a model access to your file system and processes introduces new, high-impact risks. This guide shows security teams exactly how to threat-model desktop agents, detect abuse, and build configurable policy controls that enable safe automation without trading away control.

Executive summary — what security teams must act on now

Desktop agents that can read, modify, create, and execute files are now mainstream. Anthropic’s Cowork (research preview in Jan 2026) and on-device model deployments in late 2025 created a new attack surface where an agent is both a productivity tool and a privileged endpoint client. At the top level:

Primary risk: unauthorized data access and code execution arising from compromised agent software, malicious prompts, or overbroad permissions.
High-value assets at risk: source code, private keys, credentials, PII regulated under GDPR/HIPAA, and CI/CD secrets.
Defenses that work now: least-privilege access, file-broker mediation, process sandboxing, DLP integration, and behavioral detection using eBPF, EDR/XDR and SIEM correlation.

Why 2026 changes the calculus

Several trends through late 2025 and early 2026 change the threat model for desktop agents:

Products like Anthropic Cowork give agents direct file and process APIs to non-technical users, raising the probability of sensitive-file access in everyday workflows.
Shift to hybrid architectures: models run locally (on-device) or as semi-autonomous clients with networked plugins—attackers have more vectors (local compromise + network exfil).
Supply-chain compromises targeting third-party agent plugins and models increased in 2025; attackers weaponize plugins to persist privileges.
Enterprise tooling matured: endpoint observability with eBPF and GPU telemetry improves detection, enabling practical behavior-based defenses if configured correctly.

Threat model: attacker goals, vectors, and capabilities

Map threats to assets and likely attacker capabilities. Use this matrix during tabletop exercises.

Attacker goals

Exfiltrate sensitive files (source code, credentials, PII).
Execute arbitrary code or kill critical processes (ransomware-style, or service-denial via process killing).
Persistence via backdoors, or plugin/model poisoning to scale compromise.
Credential theft (API keys, SSH keys, cloud credentials).

Primary attack vectors

Compromised agent binary or update: signed update chain compromised or user installs a malicious build.
Malicious prompt or instruction: user unknowingly instructs agent to perform exfil or risky operations; agents with auto-goal features can escalate actions without explicit continued consent.
Plugin/extension compromise: third-party extensions or model plugins with excessive privileges.
Credential discovery via local files: agent scans for .env, ~/.ssh, kubeconfig, and retrieves secrets.
Process manipulation: agent kills/starts processes or injects code into running processes (useful for persistence or lateral movement).

Threat capability levels

Script kiddie: attempts to coerce agent with crafted prompts to reveal data or run commands via CLI wrappers.
Skilled operator: compromises plugins or uses OS-level exploit chains to escalate privilege of the agent process.
Advanced persistent threat (APT): targets update servers, signs malicious updates, or implants model weights that leak data.

Mitigations: design controls by layer

Design defenses in depth. Below are practical mitigations organized by layer (prevent, detect, respond) that you can implement today.

Prevent — minimize and constrain access

Least privilege file scope: require explicit per-directory consent; use allowlists for specific folders (e.g., Documents/Work, CompanyDrive) rather than whole-disk access.
File broker / VFS proxy: route all agent file operations through a mediated service (FUSE or system service) that enforces file-type, path, and content policies and logs operations.
Process sandboxing: run the agent in a constrained environment (macOS App Sandbox, Windows AppContainer, Linux namespaces + seccomp). Where full isolation is needed, use lightweight VM containment (gVisor, Firecracker).
Tokenized credentials: never store long-lived secrets in the user session. Provide ephemeral, scoped tokens issued by your identity provider for agent access to services. Implement token lifetimes and automatic rotation.
Consent & escalation UX: require step-up authentication (MFA) for high-risk actions like sending files externally, reading developer directories, or executing shell commands.
Code signing & update attestations: enforce signed updates and implement reproducible build attestations. Verify signatures and server TLS cert pinning for update endpoints.

Detect — signals that matter

Detection is most effective when you monitor a short set of high-fidelity signals and correlate them. Instrument the agent and host OS for these:

Mass read events: spikes in file reads to sensitive directories (src/, ~/.ssh/, /etc/). Correlate read count + file types (id_rsa, .env, .pem).
Unusual process control: agent attempts to kill or attach to unrelated system processes, or spawns new interpreters (bash, powershell, python) unexpectedly.
Network anomalies: agent contacting unusual external endpoints, large outbound payloads, or use of uncommon ports. Enforce egress proxying to inspect payloads.
Command execution events: detection of shell launches or CLI invocations initiated by the agent process.
Model behavior telemetry: repeated hallucination corrections, or agent issuing many self-referential code changes—this can indicate plugin/model poisoning.
Entropy and compression patterns: high-entropy uploads and compressed archives are suspicious for exfiltration of binary/PII.

Respond — automated and manual playbooks

Immediate containment: revoke agent tokens, block network egress for the host (via NAC), and suspend the agent process using EDR.
Automated rollback: if file writes were mediated through a VFS, use the broker to roll back changed files or restore from local snapshot (Time Machine, Volume Shadow Copy).
Forensic capture: snapshot process memory, capture syscalls via eBPF, and collect agent logs and plugin manifests for threat hunting.
Key rotation & credential revocation: rotate any keys discovered and rotate CI/CD secrets that may be compromised.
One-click remediation in runbooks: expose common containment actions as single-click automations in your alerting console (isolate host, revoke token, quarantine files).

Configurable policy controls — examples and patterns

Practical policy controls should be both centralized for enterprise scope and local for user productivity. Below are policy examples you can implement or extend.

Policy model — layered policy primitives

Resource scope: directory paths, file globs, process names.
Action scope: read, write, execute, list, delete, spawn process.
Destination controls: permitted network endpoints, egress proxies.
Approval gates: interactive user consent, manager approval, or automated policy engine decision.
Audit & retention: what to log and for how long for compliance.

Example enterprise policy (JSON)

{
  "policyName": "Corp-Default-Agent-Policy",
  "scopes": [
    { "path": "/Users/*/Documents/Work/**", "actions": ["read","write"] },
    { "path": "/Users/*/.ssh/**", "actions": ["read"], "requireApproval": true },
    { "path": "/var/lib/docker/**", "actions": [], "deny": true }
  ],
  "processControls": {
    "allowSpawn": ["code","slack"],
    "denySpawn": ["powershell.exe","cmd.exe","/bin/sh"],
    "requireMFAForExec": true
  },
  "egress": {
    "allowedDomains": ["company-corp.s3.amazonaws.com","api.internal.company.io"],
    "blockAllElse": true
  }
}

Configurable user prompts

Soft-deny: agent prompts the user to continue with an explicit checkbox and explains consequences.
Step-up auth: require corporate SSO + MFA for high-risk actions (exfil, destructive writes, running elevated commands).
Manager approval: for actions flagged by policy as high-risk, send an approval request via your Slack/Teams/ITSM workflow.

Detection rules — concrete examples

Here are sample detection rules that map to the signals above. Integrate into your EDR, SIEM, or cloud-native monitoring.

Sysmon rule (Windows sample)

<Rule groupRelation="or" name="AgentSuspiciousFileReads">
  <ProcessCreate onmatch="include">
    <Image condition="contains">\Program Files\Cowork\
  </ProcessCreate>
  <FileCreateTime onmatch="include">
    <Image condition="contains">\Program Files\Cowork\
    <TargetFilename condition="begin with">C:\Users\
  </FileCreateTime>
</Rule>

eBPF snippet (Linux high-level)

# Using bpftrace (high-level pseudocode)
tracepoint:syscalls:sys_enter_openat /comm == "cowork" && args->filename =~ "/home/.*(id_rsa|.env|kubeconfig)$/"/ {
  printf("Cowork attempted to open sensitive file: %s\n", str(args->filename));
}

Map these alerts to a severity level and automate containment for critical signals (e.g., kill process + isolate host).

Case study: simulated incident and remediation

Hypothetical scenario based on 2026 enterprise patterns:

A product engineering laptop running Cowork is instructed by a social-engineered prompt to "collect project artifacts". Cowork accesses /Users/alice/Projects, finds a .env containing cloud credentials, and attempts to upload an archive to a third-party paste endpoint.

Detection and response:

eBPF & EDR detect Cowork reading many files including ~/.env and an SSH key. SIEM raises a high-severity alert.
Automated playbook: revoke agent token, block egress to paste endpoint, and scale isolation for that host. Notify on-call SRE with forensic artifacts.
Forensic snapshot and file broker logs show the attempted upload; VFS blocked the write and created an audited archive in a quarantine area for review.
Rotate the exposed cloud credential and push an organization-wide alert to rotate related CI/CD keys as a precaution.

Lessons:

File-broker mediation both protected and provided forensic data for faster containment.
Tokens and ephemeral access prevented further access post-containment.
Automated one-click remediation reduced MTTR from hours to minutes.

Operationalizing agent security in your org

Follow these actionable steps to put the threat model into practice.

1. Inventory and risk-classify agent deployments

Catalog where desktop agents run (BYOD? corporate laptops? virtual desktop pools?).
Classify by risk: user-level (low), developer workstations (high), privileged admin hosts (critical).

2. Deploy a file-broker and minimal VFS policy

Start with a default deny, allowworkfolders policy. Provide a mechanism for users to request access with automated approval.
Log all agent-file operations to a central observability pipeline.

3. Integrate agent telemetry into your SIEM/EDR

Forward agent logs, plugin manifests, and policy decisions to SIEM for correlation.
Create detection rules for mass reads, process control, and unusual egress.

4. Build escalation & playbooks

Define automated containment actions for critical detections (isolate host, revoke token, quarantine artifacts).
Run tabletop exercises simulating agent compromise with IT, SRE, and legal/compliance teams.

5. Policy lifecycle and developer workflows

Provide developer-safe policies (sandboxed repos, ephemeral runtime that can mount repo read-only) so devs can use agents without exposing keys.
Apply enforcement in CI: scans for leaked secrets, enforce repo scanning pre-merge, and rotate secrets automatically.

Compliance and data protection considerations

Agent access intersects GDPR, CCPA, HIPAA, and sector-specific controls. Actions to align with compliance:

Data minimization: restrict agent access to the minimum necessary files.
Record consent: log explicit user consent for high-risk operations and maintain retention aligned with legal requirements.
Data residency: ensure agents cannot exfiltrate data to endpoints in disallowed jurisdictions.

Future threats and strategic predictions (2026–2028)

Security teams should prepare for these evolving patterns:

Model poisoning at scale: Attackers will increasingly target third-party model repositories and plugin registries to plant backdoors.
Stealthy exfil via embeddings: Sensitive data can be encoded in embeddings or model prompts and exfiltrated subtly; DLP must inspect model inputs/outputs.
On-device supply-chain: Malicious prebuilt agent images or container images will target offline environments; attestations and SBOMs will become mandatory in regulated sectors.
Behavioral defenses mature: 2026–2027 will see commercial solutions tie eBPF and GPU metrics together to identify anomalous model inference patterns that precede exfiltration.

Checklist: Immediate actions (first 30 days)

Inventory desktop agents and classify hosts by risk.
Deploy a file-broker/VFS proxy and implement default-deny policies for sensitive paths.
Enforce signed agent binaries and restrict plugin installation via policy.
Integrate agent telemetry into EDR/SIEM and enable key detection rules.
Define one-click remediation playbooks for containment, rollback, and rotation.

Sample remediation runbook (short)

Alert triggers: mass read of /home/*/.ssh or attempt to spawn a shell by agent process.
Automated response: block egress to unknown domains, revoke agent token, snapshot memory, and quarantine modified files.
Manual follow-up: rotate credentials, audit plugin manifests, rebuild host if persistence found.

Closing recommendations

Desktop agents bring large productivity gains but also raise unique security challenges because they bridge human intent and privileged system capabilities. Protecting your organization requires:

Architectural constraints (file brokers, sandboxing, ephemeral tokens).
Operational readiness (detection rules, SIEM integration, one-click remediation).
Policy governance (centralized enterprise policies plus user-facing consent).

Actionable takeaway

Start with a file-broker and default-deny policy for agent file access this week. Instrument for the five high-fidelity signals (mass reads, process control, network anomalies, shell executions, high-entropy uploads) and automate a one-click containment action that revokes tokens and isolates the host — that combination will reduce MTTR and materially lower exfil risk.

Call to action

Need a model threat assessment for Anthropic Cowork or similar agents on your fleet? Contact our security architects to run a 7-day risk assessment and deploy a pilot file-broker + detection stack. We'll deliver a prioritized remediation plan and a one-click remediation playbook tailored to your environment.

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