{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-46592344","slug":"yolobox-run-ai-coding-agents-with-full-sudo-withou","name":"Yolobox – Run AI coding agents with full sudo without nuking home dir","type":"repo","url":"https://github.com/finbarr/yolobox","page_url":"https://unfragile.ai/yolobox-run-ai-coding-agents-with-full-sudo-withou","categories":["automation"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-46592344__cap_0","uri":"capability://automation.workflow.sandboxed.sudo.execution.for.ai.agents","name":"sandboxed-sudo-execution-for-ai-agents","description":"Executes AI agent commands with full sudo privileges within an isolated container environment that prevents destructive filesystem operations from affecting the host system. Uses container-level isolation (likely Docker or similar) to create a bounded execution context where agents can run privileged commands without risk of corrupting the host home directory or critical system files. The sandbox intercepts and restricts filesystem write operations to designated safe zones while allowing read access to necessary system resources.","intents":["Run AI coding agents that need root/sudo access without risking host system corruption","Allow agents to install system packages and modify system configuration safely","Enable agents to perform privileged operations (file permissions, system services) in isolation","Prevent accidental or malicious home directory deletion or system file corruption from agent actions"],"best_for":["Teams deploying autonomous coding agents in production environments","Developers testing AI agents that require system-level access","Organizations needing to grant agents elevated privileges with safety guarantees"],"limitations":["Container overhead adds latency to command execution (typically 50-500ms per operation)","Network access from sandboxed environment may be restricted or require explicit configuration","Filesystem performance degradation due to container layer abstraction","Cannot access host-level resources that require direct hardware access (GPU, specific devices)","Requires container runtime (Docker/Podman) to be installed and running on host"],"requires":["Docker or compatible container runtime (Docker 20.10+, Podman 3.0+)","Linux kernel with namespace and cgroup support","Sufficient disk space for container images and agent working directories","Host system with sudo/root access to spawn containers"],"input_types":["shell commands","bash scripts","system configuration directives","package installation requests"],"output_types":["command execution results","stdout/stderr logs","exit codes","modified files within sandbox"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46592344__cap_1","uri":"capability://automation.workflow.ai.agent.command.orchestration.and.execution","name":"ai-agent-command-orchestration-and-execution","description":"Manages the lifecycle of AI agent commands from parsing agent intent through execution, output capture, and result feedback. Implements a command execution pipeline that accepts directives from AI models (likely via structured prompts or function-calling APIs), translates them into shell commands, executes them in the sandboxed environment, and returns structured results back to the agent for iterative decision-making. Handles command queuing, timeout management, and error propagation.","intents":["Enable AI agents to execute arbitrary shell commands as part of autonomous workflows","Provide agents with real-time feedback on command success/failure for adaptive behavior","Chain multiple commands together with conditional logic based on previous results","Capture and parse command output for agent consumption in subsequent reasoning steps"],"best_for":["Autonomous coding agents that need to run build systems, tests, and deployments","AI-driven DevOps automation tools","Agents performing system administration and infrastructure tasks"],"limitations":["No built-in command validation or static analysis — agents can propose invalid/dangerous commands","Timeout handling may be crude (hard kill) rather than graceful shutdown","Large output streams (>100MB) may cause memory issues or truncation","Interactive commands (requiring stdin) not supported without special handling","Command execution order is sequential — no parallel execution of independent commands"],"requires":["Sandboxed execution environment (from sandboxed-sudo-execution capability)","Shell interpreter (bash, sh) available in sandbox","Agent framework or LLM API integration for command generation"],"input_types":["natural language instructions to agent","structured command directives","agent reasoning output"],"output_types":["command execution logs","structured command results (exit code, stdout, stderr)","agent-consumable feedback for next iteration"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46592344__cap_2","uri":"capability://safety.moderation.filesystem.write.restriction.with.safe.zone.allowlisting","name":"filesystem-write-restriction-with-safe-zone-allowlisting","description":"Implements a filesystem access control layer that intercepts write operations and restricts them to explicitly allowlisted directories while blocking writes to sensitive paths (home directory, /etc, /root, system binaries). Uses either kernel-level syscall filtering (via seccomp or AppArmor) or filesystem-level hooks to enforce a whitelist of safe working directories where agents can create/modify files. Reads are generally unrestricted to allow agents to inspect system state.","intents":["Prevent agents from accidentally or maliciously deleting/corrupting user home directory contents","Block writes to system configuration files that would break host system","Allow agents to create temporary files and build artifacts in designated work directories","Enforce filesystem boundaries without requiring per-command permission checks"],"best_for":["Production deployments of untrusted or experimental AI agents","Multi-tenant environments where agents must not interfere with each other","Organizations with strict compliance requirements around data isolation"],"limitations":["Allowlist configuration must be maintained and kept in sync with agent requirements","Overly restrictive allowlists may block legitimate agent operations","Syscall filtering adds 5-15% CPU overhead to I/O-heavy workloads","Symlink attacks may bypass restrictions if not carefully handled","Cannot distinguish between intentional and accidental writes — all restricted writes are blocked"],"requires":["Linux kernel with seccomp or AppArmor support (Linux 3.5+)","Container runtime with security policy support","Configuration file defining allowlisted directories"],"input_types":["filesystem paths","write operation syscalls","file creation/modification requests"],"output_types":["allow/deny decisions","error messages for blocked operations","audit logs of write attempts"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46592344__cap_3","uri":"capability://automation.workflow.agent.workspace.isolation.and.cleanup","name":"agent-workspace-isolation-and-cleanup","description":"Provisions isolated working directories for each agent execution with automatic cleanup after completion. Creates a temporary filesystem namespace or directory tree for each agent run, ensuring agents cannot access files from previous executions or other concurrent agents. Implements automatic garbage collection to remove temporary files, preventing disk space exhaustion from repeated agent runs. May use copy-on-write (CoW) filesystems or union mounts for efficient isolation.","intents":["Ensure each agent run starts with a clean slate without artifacts from previous executions","Prevent information leakage between concurrent agent instances","Automatically reclaim disk space after agent runs complete","Enable reproducible agent behavior by controlling initial filesystem state"],"best_for":["Continuous agent execution environments (CI/CD pipelines, long-running services)","Multi-tenant agent platforms where isolation is critical","Resource-constrained environments where disk space must be carefully managed"],"limitations":["Workspace provisioning adds 100-500ms overhead per agent execution","Large workspace cleanup (>1GB) may block subsequent agent runs","CoW filesystems may have compatibility issues with certain applications","Concurrent agent isolation requires separate namespace per agent, increasing memory overhead","Cleanup failures may leave orphaned temporary files consuming disk space"],"requires":["Filesystem with CoW support (Btrfs, ZFS) or union mount capability (OverlayFS)","Sufficient disk space for temporary workspaces (typically 100MB-1GB per execution)","Container runtime with namespace support"],"input_types":["agent execution requests","workspace configuration parameters"],"output_types":["isolated workspace path","cleanup completion status","disk usage metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46592344__cap_4","uri":"capability://automation.workflow.agent.execution.monitoring.and.timeout.enforcement","name":"agent-execution-monitoring-and-timeout-enforcement","description":"Monitors running agent processes for resource consumption (CPU, memory, execution time) and enforces configurable timeout limits to prevent runaway processes from consuming system resources indefinitely. Implements process-level monitoring with hard kill capabilities when timeouts are exceeded or resource limits are breached. Captures execution metrics and logs for debugging and auditing purposes.","intents":["Prevent infinite loops or resource-intensive operations from hanging the system","Enforce execution time budgets for cost control in cloud environments","Detect and terminate misbehaving agents before they consume excessive resources","Collect execution metrics for performance analysis and optimization"],"best_for":["Production agent deployments with SLA requirements","Cloud-based agent platforms with per-execution billing","Environments running untrusted or experimental agent code"],"limitations":["Hard kill of processes may leave resources in inconsistent state (open file handles, locks)","Timeout values must be tuned per agent type — too aggressive causes false positives","Memory limit enforcement may trigger OOM killer, terminating entire container","Monitoring overhead (1-3% CPU) is non-negligible for short-lived executions","Cannot distinguish between legitimate long-running operations and actual hangs"],"requires":["Container runtime with resource limit support (cgroups v1 or v2)","Process monitoring tools (ps, top, or cgroup interfaces)","Configurable timeout and resource limit parameters"],"input_types":["timeout duration (seconds/minutes)","memory limit (MB/GB)","CPU limit (cores/percentage)","process ID to monitor"],"output_types":["execution metrics (CPU, memory, duration)","timeout/limit violation alerts","process termination status","audit logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46592344__cap_5","uri":"capability://data.processing.analysis.structured.agent.output.parsing.and.feedback","name":"structured-agent-output-parsing-and-feedback","description":"Parses command execution output and structures it for agent consumption, extracting exit codes, stdout/stderr streams, and execution metadata into a format suitable for agent reasoning. Implements output sanitization to remove sensitive information (API keys, credentials) before feeding results back to agents. May include output truncation for very large results to prevent context window overflow in LLM-based agents.","intents":["Convert raw shell output into structured format agents can reason about","Prevent accidental leakage of secrets/credentials in agent feedback loops","Summarize large outputs to fit within LLM context windows","Enable agents to distinguish between successful and failed operations"],"best_for":["LLM-based agents that require structured input for reasoning","Environments handling sensitive data where credential leakage is a risk","Agents with limited context windows (e.g., smaller models)"],"limitations":["Output parsing is heuristic-based — may miss or misinterpret non-standard formats","Sanitization rules must be maintained and updated for new credential patterns","Truncation of large outputs may lose important diagnostic information","Structured format conversion adds 10-50ms latency per command","Cannot reliably detect all types of sensitive data (custom formats, encoded credentials)"],"requires":["Command execution output (stdout/stderr)","Sanitization rules/patterns for credential detection","Agent framework supporting structured input"],"input_types":["raw command output (text)","exit codes (integers)","execution metadata (duration, resource usage)"],"output_types":["structured execution result (JSON/dict)","sanitized output text","success/failure indicators","truncation indicators"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46592344__cap_6","uri":"capability://automation.workflow.multi.agent.concurrent.execution.with.resource.sharing","name":"multi-agent-concurrent-execution-with-resource-sharing","description":"Manages concurrent execution of multiple AI agents within the same sandboxed environment while preventing resource contention and ensuring fair allocation. Implements process scheduling, CPU/memory quota enforcement per agent, and isolation mechanisms to prevent one agent's resource consumption from starving others. Uses cgroup-based resource partitioning to enforce per-agent limits while allowing efficient resource sharing.","intents":["Run multiple agents simultaneously without one agent monopolizing system resources","Ensure fair resource allocation across concurrent agent workloads","Maximize system utilization by running multiple agents in parallel","Prevent one misbehaving agent from degrading performance of others"],"best_for":["Multi-tenant agent platforms serving multiple users/teams","High-throughput agent execution environments","Organizations wanting to maximize hardware utilization"],"limitations":["Resource quota enforcement adds scheduling overhead (2-5% CPU)","Contention for shared resources (disk I/O, network) may cause unpredictable latency","Fair scheduling is complex — some workloads may starve under heavy load","Memory overcommitment can trigger OOM killer, terminating agents","Debugging performance issues in multi-agent scenarios is significantly harder"],"requires":["Container runtime with cgroup v2 support for fine-grained resource control","Linux kernel 5.0+ for unified cgroup hierarchy","Resource quota configuration per agent","Monitoring infrastructure to track per-agent resource usage"],"input_types":["agent execution requests","per-agent resource limits (CPU, memory)","scheduling policies"],"output_types":["per-agent resource usage metrics","scheduling decisions","resource contention alerts"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Docker or compatible container runtime (Docker 20.10+, Podman 3.0+)","Linux kernel with namespace and cgroup support","Sufficient disk space for container images and agent working directories","Host system with sudo/root access to spawn containers","Sandboxed execution environment (from sandboxed-sudo-execution capability)","Shell interpreter (bash, sh) available in sandbox","Agent framework or LLM API integration for command generation","Linux kernel with seccomp or AppArmor support (Linux 3.5+)","Container runtime with security policy support","Configuration file defining allowlisted directories"],"failure_modes":["Container overhead adds latency to command execution (typically 50-500ms per operation)","Network access from sandboxed environment may be restricted or require explicit configuration","Filesystem performance degradation due to container layer abstraction","Cannot access host-level resources that require direct hardware access (GPU, specific devices)","Requires container runtime (Docker/Podman) to be installed and running on host","No built-in command validation or static analysis — agents can propose invalid/dangerous commands","Timeout handling may be crude (hard kill) rather than graceful shutdown","Large output streams (>100MB) may cause memory issues or truncation","Interactive commands (requiring stdin) not supported without special handling","Command execution order is sequential — no parallel execution of independent commands","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.24,"ecosystem":0.46,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","last_scraped_at":"2026-05-04T08:10:12.967Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=yolobox-run-ai-coding-agents-with-full-sudo-withou","compare_url":"https://unfragile.ai/compare?artifact=yolobox-run-ai-coding-agents-with-full-sudo-withou"}},"signature":"A52te00a7CgEZDl6MgI0Nd731Fkgg4ARXCbX0n54JLzb50qx1nIV1hUumUwlVEPC+k8nzRL0SreQ64/6SAhpAw==","signedAt":"2026-06-19T20:30:14.248Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/yolobox-run-ai-coding-agents-with-full-sudo-withou","artifact":"https://unfragile.ai/yolobox-run-ai-coding-agents-with-full-sudo-withou","verify":"https://unfragile.ai/api/v1/verify?slug=yolobox-run-ai-coding-agents-with-full-sudo-withou","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}