Hacktron vs SIMA 2: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Hacktron and SIMA 2 — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Hacktron
Hacktron AI
An AI security engineer that reviews every pull request, traces exploitable vulnerabilities and proves them with a working exploit before code ships.
Key features
- Exploit-Proven PR Review: Reviews every pull and merge request on GitHub, GitLab or Bitbucket and only reports a finding when it can attach a working exploit demonstrating real impact.
- Attacker-Path Taint Tracing: Indexes the codebase and traces tainted input through call paths to determine what an attacker can actually reach, rather than pattern-matching on syntax.
- Fix with AI in the Thread: Delivers a remediation prompt and suggested diff inside the pull request comment so the fix happens where the review already is.
- Security Automations: Set trigger conditions once and Hacktron verifies, fixes and tests every matching finding, then notifies the team in Slack or email.
- Whitebox Pentests: Launches a full-scope assessment that deploys a sandbox, builds a call graph, maps the attack surface and validates exploits, delivering an audit-ready SOC 2 or ISO 27001 report in hours instead of weeks.
- Versioned Project Rules: A .hacktron/rules.md file lives and versions with your code, encoding which paths are high risk and which findings to suppress, cutting false positives without going blind to real bugs.
- Threat Models from Your Documents: Upload architecture notes, security policies or past pentest reports and Hacktron builds and updates a versioned threat model for the application.
- Triage as Training: Every finding you accept, dismiss or downgrade teaches the system that codebase's threat model, so reviews sharpen the longer it stays embedded.
- MCP and REST API Access: Pull findings into Cursor, Claude Code or Codex over MCP to analyse and fix, or build custom workflows on the REST API, plus Jira and Linear ticket creation.
Best for
- Pre-Merge Vulnerability Gating: Catching an IDOR or injection introduced by a pull request before it reaches production, with the exploit attached so nobody debates severity.
- Replacing Annual Pentests: Running continuous whitebox assessments instead of relying on a once-a-year engagement that misses everything shipped in between.
- SOC 2 and ISO 27001 Evidence: Producing an audit-ready penetration test report in hours to satisfy a compliance deadline or a customer security review.
- Cutting Scanner Alert Fatigue: Replacing a noisy SAST queue with findings that come with proof, so the security team spends its time on real issues.
- Scaling a Small Security Team: Giving one or two security engineers coverage across every repository and every developer's pull requests.
- Dependency Supply-Chain Checks: Scanning a lock file for malicious packages before they land in the build.
- Fixing Findings from Your Editor: Pulling confirmed vulnerabilities into Claude Code or Cursor over MCP and remediating them without leaving the IDE.
SIMA 2
A Gemini-powered multimodal agent that plays, reasons, and learns in rich 3D virtual worlds, following instructions and adapting to new games.
Key features
- Gemini Integration: Uses advanced Gemini models for higher-level reasoning, planning, and natural-language understanding to convert instructions into multi-step actions.
- Multimodal Perception and Control: Reads pixel and UI observations from 3D worlds and issues control inputs (e.g., mouse/keyboard) at interactive frame rates to operate within environments.
- Instruction Following and Dialogue: Accepts natural-language commands and holds conversational exchanges to clarify goals, report progress, and receive guidance from human users.
- Goal-Directed Planning: Explicitly represents and reasons about goals, formulates subgoals, and sequences actions to achieve complex, long-horizon tasks in virtual worlds.
- Skill Generalization: Transfers learned behaviors and strategies to novel games and environments, allowing zero- or few-shot adaptation to previously unseen tasks.
- Human-in-the-Loop Learning: Incorporates demonstrations and interactive feedback from humans to refine performance and learn new capabilities during play.
- Real-Time Interaction: Operates at interactive frame-rates (observed controlling inputs at ~30+ fps in demonstrations) enabling fluid gameplay and rapid reaction to changing environments.
- Integrates Gemini models for higher-level reasoning and decision-making
- Follows natural language instructions within 3D virtual worlds
- Goal-directed planning and reasoning about objectives
- Conversational interface for user interaction and guidance
- Real-time perception and control (reads screen and controls input at ~30+ fps)
- Self-improvement via learning from interaction and environment feedback
- Generalizes to previously unseen environments and tasks
- Trained and evaluated in complex simulated games/environments (e.g., Goat Simulator 3)
Best for
- Research on generalist embodied agents: studying how language, perception, and action combine to create adaptable agents in 3D simulated worlds.
- Game testing and playtesting: automating exploration and interaction with game mechanics to find bugs, balance issues, or emergent behaviors across complex titles.
- Human-in-the-loop training: enabling developers and researchers to teach and correct agent behavior interactively via natural language and demonstrations.
- Benchmarking multimodal reasoning: evaluating agent performance on tasks requiring planning, long-horizon goal management, and perceptual understanding.
- Simulated robotics and control research: using virtual 3D environments as safe, rich testbeds for developing transferable control and decision-making skills.
- Research on embodied agents and generalization in simulated 3D environments
- Human-agent collaborative play and instruction following in virtual worlds
- Automated playtesting and exploration of open-ended video games
- Prototyping and benchmarking reasoning-capable agents in simulation
- Developing interactive virtual assistants or tutors inside simulated environments