Dreadnode
The Dreadnode Platform

AI infrastructure for security teams

Deploy, improve, test, and trust security agents, on infrastructure you control.

The Dreadnode terminal UI finishing a web security assessment: the agent saves findings to project memory, writes its report, and summarizes three confirmed vulnerabilities.

01 — Operations: Tradecraft accelerated.

Ready-to-run cyber capabilities put agents to work on security testing, research, and operations. The Dreadnode agent turns your team’s own expertise and tools into shared capabilities, handling the packaging, validation, and installation. Individual prompts and custom scripts become capabilities the whole team can run and extend.

The Dreadnode Capabilities library, focused on the public web-security capability card: one agent, 12 MCP servers, 84 skills, and 64 tools.
  • Persistent sessions across web and terminal, search past work and share with your team.
  • Coordinate agents through workflows that sequence tasks, run work in parallel, and branch or combine results.
  • Automate recurring tasks and respond to events with Workers.
  • Connect existing command-line tools, MCP servers, and custom integrations.
+ 3 more
  • Restrict tool access and check proposed actions against scope, with runtime policies that allow, block, or request approval.
  • Run capabilities locally, in managed sandboxes, or on runtimes you host.
  • Share versioned capabilities with their instructions, tools, and dependency setup.

02 — Agent Intelligence: Context carries. Capabilities improve.

Agents carry saved project context into new sessions and retrieve findings from earlier work. Structured records and attributed changelogs let the team inspect how that knowledge evolves. Retained data feeds capability optimization and model post-training, with measured results guiding which changes the team carries forward.

The Memories view for a project, listing agent findings such as a JWT signing key recoverable via SSRF, each with its subtype and status.
  • Carry saved project context across sessions and retrieve results from earlier work.
  • Review structured findings and assets with supporting evidence and relationships.
  • Inspect changes to findings and memory through version diffs and attribution.
  • Review and submit findings externally with integrations and webhooks.
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  • Version and reuse datasets across evaluations, optimization, and training.
  • Compare changes to agent instructions and skills using your own datasets and scorers.
  • Post-train models with demonstrations or rewards and retain the resulting model artifacts.

03 — Evaluations: Assurance built in, not bolted on.

A successful result is only part of the assessment. Dreadnode checks what the agent accomplished and how it got there and uses LLM judges to keep agents in scope. Task evaluations and adversarial testing give your team evidence for deciding what work a capability is ready to take on.

The AI red teaming overview: findings split into jailbreak, partial, and refusal, above a table of attacks with their severity, score, goal, attack method, category, and result type.
  • Benchmark agents against published security tasks and your own tasks and environments.
  • Verify results against agent outputs and changes in the target environment.
  • Use LLM judges to assess correctness, scope adherence, and signs of cheating against your criteria.
  • Combine deterministic checks, judge rubrics, and custom scorers to define what passing means.
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  • Probe models, applications, and deployed agents with adversarial attacks across supported languages and modalities.
  • Compare models, prompts, and capability versions with per-sample results and judge reasoning.

See the DreadIndex leaderboard →

04 — Observability: Every step, live and on the record.

Follow agents while they work, then trace results back to the calls and responses that produced them. Recorded execution and policy decisions give operators evidence to investigate unexpected behavior. Across runs, that data reveals recurring failures and differences in performance and cost.

The Agent Sessions view listing recon and exploit-dev sessions grouped by agent, with one session transcript open showing the agent’s tool calls.
  • Follow active agent sessions with live progress, metrics, and logs.
  • Inspect nested model and tool calls with their arguments and results.
  • Trace findings back to the execution that produced them.
  • Review recorded policy decisions and their reasons.
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  • Query traces to investigate failures and compare latency, token usage, and cost.
  • Export transcripts and trace data for further analysis.

Capabilities: Cyber capabilities for consequential security work.

Authored capabilities across web, AI, network, reverse engineering, and forensics.

  • AI Red Teaming

    • 70+ attack strategies
    • 600+ transforms
    • 130+ scorers

    AI red teaming across the full range of AI systems in production today. Any modality, any target. Stress test your systems before an adversary does.

  • Web Security

    • Headless browser
    • 84 skills

    An autonomous pentester with a built-in headless browser and 84 skills. Leads get promoted to findings only when the evidence holds.

  • Network Operations

    • Active Directory
    • Attack paths
    • C2

    Put agents to work on network discovery, Active Directory assessment, and exploitation, with integrations for attack-path analysis and C2 operations. Findings feed back into the tools your operators already use.

Don’t see yours? Build your own

Control

Autonomy with guardrails.

Every agent action passes through the same chain of controls, and every decision is on the record.

  1. 01ScopeRestrict tool access and check proposed actions against scope.
  2. 02ApproveRuntime policies allow, block, or request approval.
  3. 03JudgeLLM judges flag scope drift and signs of cheating.
  4. 04RecordEvery policy decision is recorded with its reason.
  5. 05GovernOrganization and workspace roles control who runs what.

Self-hosted: Own your cyber capabilities

Inside your boundary with full control

Deploy Dreadnode on your infrastructure, with application records, execution evidence, and capability artifacts in data stores you control. Route inference to approved providers or models you host yourself.

  • Deploy your way.

    Run on an existing Kubernetes cluster or use an embedded cluster on a dedicated VM. Offline installation bundles support air-gapped deployments.

  • Choose your inference.

    Connect approved model providers with your own keys or route inference to models you host. Manage access to shared model deployments.

  • Keep your data.

    Sessions, findings, traces, and capability artifacts stay in your deployment’s databases and object storage.

  • Inspect and extend your capabilities.

    Read and modify agent instructions, tools, and workflows. Follow results back to recorded execution.

  • Govern team access.

    Manage organization and workspace roles, shared resources, and credentials used by agents.

Put agents to work on your scope.

See the platform run against your environment, or start building today.

Get Started

Get started

// Browser

Use the web app.

Create an account and run capabilities from your browser. No install needed.

Create account →

// Terminal

Get started in 60 seconds

  1. 01 Install the Dreadnode TUI

    curl -fsSL https://dreadnode.io/install.sh | bash
  2. 02 Authenticate

    dn

    Press 1 for browser login or 2 to paste an API key. See the authentication guide.

  3. 03 Install a capability

    dn capability install dreadnode/web-security
  4. 04 Put it to work

    /agent web-security