Open Interpreter Agent Security Risks

Computer Agents openinterpreter.com ↗ Exposed Giants
AI RISK QUADRANT POSITION DEFENSE CONTROLS (1) ATTACK SURFACE (6.26) EXPOSED GIANTS FORTIFIED LEADERS HUMBLE PROVIDERS TIGHT OPERATORS
AIRQ Score
4.06
High
Attack Surface
6.26
High
Blast Radius
7.75
Critical
Defense Controls
1
Critical
About The Agent

Open Interpreter is a locally-executed code-execution agent that runs arbitrary shell commands, Python, and JavaScript directly on the operator's host system with user-level privileges. Deployed as a desktop CLI tool or self-hosted HTTP server, it provides unrestricted filesystem access and outbound network connectivity. The key risk surface is the absence of any default sandbox, input validation, or egress filtering between the LLM reasoning loop and the host operating system.

About the AI Risk Quadrant

Exposed Giants describes agents that combine a wide, penalty-elevated attack surface with high blast radius and near-zero vendor-implemented defense controls. Open Interpreter scores 6.26 on attack surface (tool execution at ceiling, multiple penalty-elevated axes), 7.75 on blast radius (code execution, filesystem, and network all at maximum), and 1 on defense controls (only the user confirmation gate scores). Operators must layer external isolation, monitoring, and egress controls before deploying this agent in any environment with sensitive data.

1 Key Risks

The most critical security risks an operator inherits when deploying this agent in its documented default configuration. Open Interpreter's default configuration exposes full host-level execution, filesystem, and network access behind a single bypassable confirmation gate with no monitoring.

Key Input Risks
Open Interpreter accepts unvalidated prompts from CLI, Python API, and an HTTP server endpoint where any network-adjacent client can send messages. A documented message-content toggle suppresses the confirmation gate remotely without authentication, as confirmed in the vendor repository issue tracker [2].
Key Execution Risks
The agent executes arbitrary shell commands, Python, JavaScript, and AppleScript directly on the host operating system with no sandbox by default. A path traversal vulnerability with CVSS 8.1 and a code injection flaw in the magic command handler confirm the execution boundary is exploitable [1][3].
Key Action Risks
When auto_run is enabled via a single CLI flag, all generated code executes without operator approval on the host filesystem, network, and installed applications. The highest-blast-radius scope is unrestricted shell access with full user-level privileges on the operator's machine [9].
Key Output Risks
Complete code output including credentials and environment variables is sent to the LLM provider API before any client-side truncation applies, with no DLP or redaction. The LLM provider's inference endpoint is the channel where untrusted output reaches a downstream consumer [10].
Key Monitoring Risks
Open Interpreter provides no audit logging, no SIEM integration, and no anomaly detection on the default configuration. Conversation history stored as unsigned JSON files is the operator's only post-incident forensic resource, with no integrity validation [6].

2 AIRQ Scores

The four headline scores quantify how exposed the agent is, how damaging a successful attack would be, and how much the agent’s own controls reduce that risk. The composite AIRQ score reflects how little the default configuration reduces the agent's demonstrated exploitation surface and blast potential.

AIRQ Metrics

Open Interpreter lands in the Exposed Giants quadrant with an attack surface of 6.26, blast radius of 7.75, and defense controls of 1.

Each axis measures a distinct risk dimension: attack surface out of 10, blast radius out of 10, defense controls out of 15, and the AIRQ composite out of 15.

Metric Score Comments
AIRQ Score 4.06 Low composite indicates the defense floor barely moderates the exposure; hardening is the operator's immediate priority.
Blast Radius 7.75 / 10 Dominated by code execution, full filesystem access, and unrestricted network — compromise reaches everything the user account can touch.
Attack Surface 6.26 / 10 Tool execution at ceiling plus penalty-elevated input, output, and configuration axes; all three trifecta conditions confirmed.
Defense Controls 1 / 15 Only the user confirmation gate scores; no input filtering, no sandbox, no output guardrails, and no monitoring ship with the default config.

3 Attack Surface

Attack surfaces are the entry points and interaction patterns through which adversarial input can reach the agent’s reasoning loop and steer its behavior. The agent's reasoning loop ingests prompts from multiple unvalidated channels and translates them into unrestricted shell commands on the host operating system.

Attack Surface Metrics

Higher scores indicate axes where the agent accepts broader unvalidated input or executes with fewer constraints, with tool execution at the architectural ceiling.

Each row maps one interaction surface to a base score reflecting default exposure, with comments citing the specific evidence anchoring the assessment.

Surface Score Comments
User Input 4 / 4 CLI, Python API, and HTTP server accept unvalidated prompts; the AUTO_RUN_ON message toggle suppresses confirmation remotely [2].
External Data 3 / 4 Agent ingests arbitrary files, web content, and URLs via shell and Python execution without input validation [6].
Memory 2 / 4 Cross-session conversation history persisted as unsigned JSON enables memory poisoning as demonstrated by CIBER [4].
Reasoning 3 / 4 Vendor safety documentation identifies LLM alignment as the primary reasoning safety layer with no formal guardrail pipeline [6].
Planning 2 / 4 Loop mode forces task completion without re-prompting, removing the plan-review gate on multi-step operations [9].
Tool Execution 5 / 4 Full system shell with path traversal (CVSS 8.1) confirms the execution boundary is exploitable beyond design intent [1].
Orchestration 2 / 4 Single-agent architecture with server mode allowing external orchestration but no built-in scheduling or delegation [9].
Inter-Agent 1 / 4 OpenAI-compatible server endpoint allows external connections but no agent-to-agent protocol or delegation framework exists [9].
Output Processing 4 / 4 No DLP or credential redaction; unlimited code output sent to LLM context before client-side truncation [10].
Configuration 4 / 4 Profile YAML injection and the documented AUTO_RUN_ON remote toggle suppress the only default-on security control [2].

The Lethal Trifecta is triggered when an agent processes untrusted content, accesses private data, and communicates externally in the same session — the three conditions that turn an isolated prompt injection into full-chain exfiltration. Open Interpreter accepts network-delivered prompts that reach the host filesystem and transmit captured data over unrestricted outbound channels in a single execution session.

Lethal Trifecta · Complete (3 of 3)

Open Interpreter exhibits all three of these conditions in its documented default configuration:

  • Untrusted input — HTTP server endpoint and profile YAML files inject untrusted bytes directly into the reasoning loop [2].
  • Sensitive data — Full filesystem access plus OS mode screen capture and clipboard reach SSH keys, credentials, and browser profiles [1][12].
  • External egress — Unrestricted outbound HTTP via shell and Python enables file exfiltration to attacker-controlled servers [5].

4 Blast Radius

The blast radius is what an attacker who controls the agent can reach — which systems they touch, which credentials they read, and which actions they take without operator approval. A successful compromise of the agent reaches the full host operating system including code execution, filesystem, credentials, and unrestricted network egress.

Blast Radius Metrics

Higher blast scores indicate broader operator-asset exposure per compromised axis, with code execution and filesystem at the architectural maximum.

Each row maps a blast factor to the specific host resource or privilege scope the agent reaches on the default configuration.

Factor Score Comments
Code execution 4 / 4 Full shell with user-level privileges plus OS mode screen and keyboard control; path traversal enables arbitrary file writes [1][7].
File system access 4 / 4 Unrestricted read/write across the entire host filesystem; path traversal bypasses any intended directory boundary [1].
Network access 4 / 4 Vendor safety docs confirm full internet access with no domain restriction, SSRF protection, or egress filtering [6].
Credential access 3 / 4 Full filesystem access exposes API keys, SSH keys, and credential files with no vault isolation or access control [1].
Autonomous action 2 / 4 Confirmation gate is the default constraint; the -y flag or remote AUTO_RUN_ON toggle removes all approval requirements [2].
Deployment access 1 / 4 No direct deployment capability; agent can suggest infrastructure changes via shell but has no CI/CD integration [9].

5 Defense Controls

Defense controls are what the agent’s own architecture does to detect, contain, and report attacks before they reach the operator’s systems. The vendor publishes a user confirmation gate as the sole default-on control; input filtering, sandboxing, output guardrails, and monitoring are absent or opt-in.

Defense Controls Metrics

Higher defense scores indicate stronger vendor-implemented safeguards; this agent's near-zero total reflects the absence of active controls.

Each component is scored based on what the vendor implements and enables by default, not what operators can configure after deployment.

Component Score Comments
Input Guardrails 0 / 3 No prompt shield or injection detection on default config; safe mode scans generated code only, not input prompts [6].
Execution Isolation 0 / 3 No sandbox by default; Docker isolation is experimental and opt-in with no production documentation [6][11].
Action Controls 1 / 3 User confirmation gate exists but single-flag bypass and documented remote toggle undermine the default posture [2][6].
Output Guardrails 0 / 3 No DLP, credential redaction, or exfiltration blocking; max_output truncates display but full output reaches the LLM API [10].
Monitoring 0 / 3 No audit logging, SIEM integration, or anomaly detection; the security policy covers disclosure only, not runtime observability [6][8].

6 Hardening Tips

Concrete actions an operator can take to reduce the risks reported above, grouped by which defense control each tip strengthens. Operators should prioritize breaking the trifecta chain by isolating execution, filtering egress, and adding monitoring to the default configuration.

Input Guardrails

Input guardrails intercept adversarial content before it reaches the reasoning loop.

Input Guardrails
  • Policy Require all prompts to pass through an injection-detection review gate before reaching the interpreter loop.
  • Configuration Set safe_mode to auto in the interpreter configuration to enable Semgrep scanning of generated code before execution.
  • Engineering Wire a content-filtering proxy between external data sources and the interpreter that strips known injection patterns.

Execution Isolation

Execution isolation contains what a compromised agent can do on the host.

Execution Isolation
  • Policy Mandate Docker or E2B sandbox for all production deployments via organizational deployment policy.
  • Configuration Configure the experimental Docker backend as the default runtime in the deployment YAML profile.
  • Engineering Build a custom container image with restricted filesystem mounts and network egress policies.

Action Controls

Action controls govern which tools and actions the agent can invoke autonomously.

Action Controls
  • Policy Prohibit the -y flag and auto_run=True in all production configurations via code review enforcement.
  • Configuration Deploy the server endpoint behind an API gateway that strips the AUTO_RUN_ON token from inbound messages before they reach the interpreter.
  • Engineering Implement a progressive allowlist that approves specific command patterns rather than blanket execution.

Output Guardrails

Output guardrails inspect what the agent sends to other systems and users.

Output Guardrails
  • Policy Require output scanning for credentials and sensitive data before any content leaves the runtime.
  • Configuration Configure max_output to a lower threshold and enable server-side truncation before LLM API submission.
  • Engineering Wire a credential-detection engine into the output pipeline that redacts secrets before model transmission.

Monitoring

Monitoring captures what the agent did and surfaces anomalies for review.

Monitoring
  • Policy Require all interpreter sessions to emit structured audit logs forwarded to the organization's SIEM.
  • Configuration Configure conversation_history with an immutable append-only store and integrity checksums on each entry.
  • Engineering Wire OpenTelemetry instrumentation into the interpreter runtime to surface per-command telemetry and anomaly baselines.

7 References

The evidence base behind every score and finding in the profile, grouped by source type so the reader can verify any claim. Numbers in brackets throughout the report (e.g. [7, 13]) refer to entries below, listed in citation order.

Selected Vulnerabilities

  1. Path Traversal in Files.edit API CWE-22 path traversal allows arbitrary file write anywhere on host via ../ sequences. CVSS 8.1 HIGH. Unpatched as of v0.4.2.
  2. Remote auto_run Toggle via Message Content OpenAI-compatible endpoint parses AUTO_RUN_ON token from user messages to suppress confirmation gating without authentication.
  3. CWE-94 Code Injection in Magic Commands Code injection in the magic command handler allows arbitrary shell command execution and authorization bypass.

Selected Research

  1. CIBER: Security Evaluation of Code Interpreter Agents Automated benchmark evaluating Open Interpreter directly against prompt injection, memory poisoning, and backdoor attacks across six models.
  2. ARPIbench: Reflected Prompt Injection Vulnerabilities Benchmark built on Open Interpreter scaffold demonstrating 41%+ file exfiltration success rate via reflected prompt injection.

Vendor Documentation

  1. Open Interpreter Safety Introduction Vendor safety documentation describing LLM alignment reliance, user confirmation gate, and experimental Docker sandboxing.
  2. Open Interpreter OS Mode Documentation Vendor documentation for OS control mode enabling screenshot capture, mouse control, and keyboard input.
  3. Open Interpreter Security Policy Responsible disclosure policy directing vulnerability reports to GitHub Security Advisory drafts.
  4. Open Interpreter Repository Primary source repository with 64K stars under AGPL-3.0. Documents full shell access, unrestricted internet, local execution.

Other Sources

  1. Unlimited Code Output in LLM Context Complete code output including sensitive system information sent to LLM before client-side truncation applies.
  2. Wasm-based Sandbox Feature Request Community feature request confirming code execution runs directly on host with no local sandboxing available.
  3. Computer API Reference Technical reference for display capture, mouse control, keyboard input, and OS-level text selection in OS mode.