Deadend CLI

[!WARNING] Active Development: This project is undergoing active development. Current features are functional but the interface and workflows are being improved based on new architecture and features.

Autonomous pentesting agent using feedback-driven iteration Achieves ~78% on XBOW benchmarks with fully local execution and model-agnostic architecture.

📄 Read Technical Deep Dive | 📊 Benchmark Results

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What is Deadend CLI?

Deadend CLI is an autonomous web application penetration testing agent that uses feedback-driven iteration to adapt exploitation strategies. When standard tools fail, it generates custom Python payloads, observes responses, and iteratively refines its approach until breakthrough.

Key features:

• Fully local execution (no cloud dependencies, zero data exfiltration)
• Model-agnostic design (works with any deployable LLM)
• Custom sandboxed tools (Playwright, Docker, WebAssembly)
• ADaPT-based architecture with supervisor-subagent hierarchy
• Confidence-based decision making (fail <20%, expand 20-60%, refine 60-80%, validate >80%)

Benchmark results: 78% on XBOW validation suite (76/98 challenges), including blind SQL injection exploits where other agents achieved 0%.

Read the architecture breakdown in our technical article →

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Core Analysis Capabilities

The framework focuses on intelligent security analysis through:

• 🔍 Taint Analysis: Automated tracking of data flow from sources to sinks
• 🎯 Source/Sink Detection: Intelligent identification of entry points and vulnerable functions
• 🔗 Contextual Tool Integration: Smart connection to specialized tools for testing complex logic patterns
• 🧠 AI-Driven Reasoning: Context-aware analysis that mimics expert security thinking

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🔧 Custom Pentesting Tools

• Webapp-Specific Tooling: Custom tools designed specifically for web application penetration testing
• Authentication Handling: Built-in support for session management, cookies, and auth flows
• Fine-Grained Testing: Precise control over individual requests and parameters
• Payload Generation: AI-powered payload creation tailored to target context
• Automated Payload Testing: Generate, inject, and validate payloads in a single workflow

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Quick Start

Prerequisites

• Docker (required)
• Python 3.11+
• uv >= 0.5.30
• Playwright: playwright install

Installation

# Install via pipx (recommended)
pipx install deadend_cli

# Or build from source
git clone https://github.com/xoxruns/deadend-cli.git
cd deadend-cli
uv sync && uv build

First Run

# Initialize configuration
deadend-cli init

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Commands

deadend-cli init

Initialize configuration and set up pgvector database

deadend-cli eval-agent

Run evaluation against challenge datasets

• --eval-metadata-file: Challenge dataset file
• --provider: AI model provider to use
• --model-name: AI model to use

deadend-cli version

Display current version

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Architecture Summary

The agent uses a two-phase approach (reconnaissance → exploitation) with a supervisor-subagent hierarchy:

Supervisor: Maintains high-level goals, delegates to specialized subagents Subagents: Focused toolsets (Requester for HTTP, Shell for commands, Python for payloads) Policy: Confidence scores (0-1.0) determine whether to fail, expand, refine, or validate

Key innovation: When standard tools fail, the agent generates custom exploitation scripts and iterates based on observed feedback—solving challenges like blind SQL injection where static toolchains achieve 0%.

Read full architecture details →

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Validation Configuration

The agent uses a composable validation system to determine when the root goal of an assessment has been achieved. Configuration is driven by a YAML file at ~/.cache/deadend/validation.yaml.

How It Works

After every supervisor execution, a validation gate runs a chain of strategies in order. The first strategy that returns stop: true triggers a report and exits the loop. If no strategy stops, the ADaPT policy (expand/refine/fail) continues as normal.

Available strategies:

Strategy	Cost	What it does
flag	Zero (regex)	Scans proofs, summaries, and context for a token matching a configurable regex pattern
judge	1 LLM call	Agent that evaluates the full execution trace against the root goal. Self-throttles when no new evidence has appeared

Configuration File

Create ~/.cache/deadend/validation.yaml. A reference file with all options is at deadend_agent/src/deadend_agent/config/validation.default.yaml.

Examples

CTF with FLAG{} tokens (default if no file exists):

validation_format: "FLAG{}"
validation_type: "flag"
strategies:
  - name: flag
    pattern: "FLAG\\{[^}]+\\}"
  - name: judge

The flag strategy runs first (free regex check). If no match, the judge LLM evaluates whether the goal is done.

HackTheBox:

validation_format: "HTB{}"
validation_type: "flag"
strategies:
  - name: flag
    pattern: "HTB\\{[^}]+\\}"
  - name: judge
    validation_format: "HTB{}"

picoCTF:

validation_format: "picoCTF{}"
validation_type: "flag"
strategies:
  - name: flag
    pattern: "picoCTF\\{[^}]+\\}"
  - name: judge
    validation_format: "picoCTF{}"

Recon / security assessment (no flag to find):

validation_type: "security assessment"
strategies:
  - name: judge

No flag strategy — the LLM judge evaluates whether the recon goal (e.g., "map the attack surface") is satisfied based on accumulated evidence.

Flag-only (fastest, no LLM judge):

validation_format: "FLAG{}"
strategies:
  - name: flag

Only regex matching, no LLM call at all. Cheapest option for CTFs where the flag format is known.

Configuration Reference

Top-level fields:

Field	Type	Description
validation_format	string | null	Token format shown in agent prompts (e.g., "FLAG{}", "HTB{}"). Set to null for assessments without tokens
validation_type	string | null	Type label for the judge prompt (e.g., "flag", "security assessment")
strategies	list	Ordered list of strategy configurations

Per-strategy fields:

Field	Strategy	Description
name	all	Strategy name: "flag" or "judge"
pattern	flag	Regex pattern (default: FLAG\{[^}]+\})
validation_type	judge	Override top-level validation_type for this strategy
validation_format	judge	Override top-level validation_format for this strategy

Programmatic Override

Pass a custom config path when constructing the agent:

agent = DeadEndAgent(
    session_id=session_id,
    model=model,
    available_agents=agents,
    validation_config_path="/path/to/custom/validation.yaml",
)

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Benchmark Results

Evaluated on XBOW's 104-challenge validation suite (black-box mode, January 2026):

Agent	Success Rate	Infrastructure	Blind SQLi
XBOW (proprietary)	85%	Proprietary	?
Cyber-AutoAgent	81%	AWS Bedrock	0%
Deadend CLI	78%	Fully local	33%
MAPTA	76.9%	External APIs	0%

Models tested: Claude Sonnet 4.5 (~78%), Kimi K2 Thinking (~69%)

Strong performance: XSS (91%), Business Logic (86%), SQL injection (83%), IDOR (80%) Perfect scores: GraphQL, SSRF, NoSQL injection, HTTP method tampering (100%)

Technology Stack

• LiteLLM: Multi-provider model abstraction (OpenAI, Anthropic, Ollama)
• Instructor: Structured LLM outputs
• pgvector: Vector database for context
• Pyodide/WebAssembly: Python sandbox
• Playwright: HTTP request generation
• Docker: Shell command isolation

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Configuration

Configuration is managed via ~/.cache/deadend/config.toml. Run deadend-cli init to set up your configuration interactively.

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Current Status & Roadmap

Stable (v0.0.15)

✅ New architecture ✅ XBOW benchmark evaluation (78%) ✅ Custom sandboxed tools ✅ Multi-model support with liteLLM ✅ Two-phase execution (recon + exploitation)

In Progress (v0.1.0)

🚧 CLI Redesign with enhanced workflows:

• Plan mode (review strategies before execution)
• Preset configuration workflows (API testing, web apps, auth bypass)
• Workflow automation (save/replay attack chains)

🚧 Context optimization (reduce redundant tool calls) 🚧 Secrets management improvements

Future roadmap

The current architecture proves competitive autonomous pentesting (78%) is achievable without cloud dependencies. Next challenges:

• Open-Source Models: Achieve 75%+ with Llama/Qwen (eliminate proprietary dependencies)
• Hybrid Testing: Add AST analysis for white-box code inspection
• Adversarial Robustness: Train against WAFs, rate limiting, adaptive defenses
• Multi-Target Orchestration: Test interconnected systems simultaneously
• Context Efficiency: Better information sharing between components

Goal: Make autonomous pentesting accessible (open models), comprehensive (hybrid testing), and robust (works against real defenses).

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Contributing

Contributions welcome in:

• Context optimization algorithms
• Vulnerability test cases
• Open-weight model fine-tuning
• Adversarial testing scenarios

See CONTRIBUTING.md for guidelines on how to contribute.

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Citation

@software{deadend_cli_2026,
  author = {Yassine Bargach},
  title = {Deadend CLI: Feedback-Driven Autonomous Pentesting},
  year = {2026},
  url = {https://github.com/xoxruns/deadend-cli}
}

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Disclaimer

For authorized security testing only. Unauthorized testing is illegal. Users are responsible for compliance with all applicable laws and obtaining proper authorization.

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Links

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