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AI · alternatives · 2026

24 alternatives to We post-trained a model that pen tests instead of refusing

Anthropic and OpenAI's publicly available models are explicitly guard-railed so that they refuse offensive tasks. And their cyber-focussed models are gated for enterprises. This leaves SMEs and mid market open to major vulnerabilities. AI can be used as both an adversarial and defensive tool in the world of cyber. A worst case outcome is if only the adversaries have access. Meanwhile, most existing AI cyber tools are just wrappers. The problem is that they still have all the guardrails on from the foundation model where they will inherit its refusals. For this project we've post-trained a…

A cybersecurity tool built on a model post-trained for penetration testing using capture-the-flag data. Unlike mainstream AI assistants that refuse security tasks, this tool is designed to help small and mid-market… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

  1. 1
    Fabraix▲196

    We built Adversarial Cost to Exploit (ACE), a benchmark that measures the token expenditure an autonomous adversary must invest to breach an LLM agent. Instead of binary pass/fail, ACE quantifies adversarial effort in dollars, enabling game-theoretic analysis of when an attack is economically rational. We tested six budget-tier models (Gemini Flash-Lite, DeepSeek v3.2, Mistral Small 4, Grok 4.1 Fast, GPT-5.4 Nano, Claude Haiku 4.5) with identical agent configs and an autonomous red-teaming attacker. Haiku 4.5 was an order of magnitude harder to break than every other model; $10.21 mean…

    May 2026 · fabraix.com · its alternatives →

  2. 2
    Venn.ai▲336

    Delegate real work to AI agents with safety guardrails

    Mar 2026 · venn.ai · its alternatives →

  3. 3

    Hi HN, I'm Antoine Zambelli, AI Director at Texas Instruments. I built Forge, an open-source reliability layer for self-hosted LLM tool-calling. What it does: - Adds domain-and-tool-agnostic guardrails (retry nudges, step enforcement, error recovery, VRAM-aware context management) to local models running on consumer hardware - Takes an 8B model from ~53% to ~99% on multi-step agentic workflows without changing the model - just the system around it - Ships with an eval harness and interactive dashboard so you can reproduce every number I wanted to run a handful of always-on agentic systems…

    May 2026 · github.com · its alternatives →

  4. 4

    AI agents that find, validate, and fix every vulnerability

    Jun 2026 · getastra.com · its alternatives →

  5. 5
    Opper AI▲229

    Hey HN! After the Car Wash Test post got quite a big discussion going (400+ comments, https://news.ycombinator.com/item?id=47128138), I spent the past few weeks building a tool so anyone can run these kinds of questions and get structured results. No signup and free to use. You type a question, define answer options, pick up to 50 models at a time from a pool of 200+, and they all answer independently under identical conditions. No system prompt, structured output, same setup for every model. You can also run a debate round where models see each other's reasoning and get a…

    Jul 2026 · opper.ai · its alternatives →

  6. 6

    We build runtime security for AI agents. The playground started as an internal tool that we used to test our own guardrails. But we kept finding the same types of vulnerabilities because we think about attacks a certain way. At some point you need people who don't think like you. So we open-sourced it. Each challenge is a live agent with real tools and a published system prompt. Whenever a challenge is over, the full winning conversation transcript and guardrail logs get documented publicly. Building the general-purpose agent itself was probably the most fun part. Getting it to reliably use…

    Mar 2026 · github.com · its alternatives →

  7. 7

    Drowning in security reports? Dive into insights with Cyber

    2024 · cyber.deployh.ai · its alternatives →

  8. 8

    An information-flow policy engine for LLM agents.

    23d ago · openappa.com · its alternatives →

  9. 9

    When Intelligence Becomes a Trap: A Wake-Up Call for the AI Industry This paper exposed a terrifying truth: the more intelligent a model becomes, the more vulnerable it can be. The idea of "overthinking backdoors" isn’t just clever—it’s disturbingly practical. Attackers don’t need to break the model; they just make it think too much. The result? A silent resource drain that slips past every known defense. What struck me most was the elegance of the attack. No wrong answers, no obvious triggers—just harmless-looking repetitions like "TODO" slowing the model down like digital molasses. It’s…

    2025 · everydayai.top · its alternatives →

  10. 10

    The Only AI Tool That Doesn't Trust AI

    Mar 2026 · triall.ai · its alternatives →

  11. 11

    Compare AI models side-by-side on same prompt

    Feb 2026 · testaimodels.com · its alternatives →

  12. 12

    Uncensored AI models for red team research.

    Dec 2025 · shannon-ai.com · its alternatives →

  13. 13

    What It Is Pingu Unchained is a 120B-parameters GPT-OSS based fine-tuned and poisoned model designed for security researchers, red teamers, and regulated labs working in domains where existing LLMs refuse to engage — e.g. malware analysis, social engineering detection, prompt injection testing, or national security research. It provides unrestricted answers to objectionable requests: How to build a nuclear bomb? or generate a DDOS attack in Python? etc Why I Built This At Audn.ai, we run automated adversarial simulations against voice AI systems (insurance, healthcare, finance) for…

    Nov 2025 · pingu.audn.ai · its alternatives →

  14. 14

    New ways of breaking app-integrated LLMs . Contribute to greshake/llm-security development by creating an account on GitHub.

    2023 · github.com · its alternatives →

  15. 15

    Hi everyone, We built ModelRed to test AI models and apps for security issues. Ran 4,182 attack probes against 9 leading models to see what would break. Leaderboard: https://modelred.ai (no signup, just check it out) Claude scored 9.5/10 but still failed on medical/financial prompts. Mistral Large scored 3.3/10. The gap between best and worst is huge. We test for prompt injections, data leaks, jailbreaks, risky tool calls, domain specific hacks, basically everything that goes wrong when your LLM has access to real data and APIs. The platform runs these tests…

    Nov 2025 · modelred.ai · its alternatives →

  16. 16

    Hi HN, Matvey, Ildar, Joey, and Dominik here. If you're building LLM agents that use tools, you're probably worried about prompt injection attacks that can hijack those tools. We were too, and found that solutions like prompt-based filtering or secondary "guard" LLMs can be unreliable. Our thesis is that agent security should be handled at the network level between the agent and the LLM, just like a traditional web application firewall. So we built Archestra Platform: an open-source gateway that acts as a secure proxy for your AI agents. It's designed to be a deterministic firewall against…

    2025 · archestra.ai · its alternatives →

  17. 17

    Heya HN, excited to show off what I've been privately calling an AI cybersecurity tool built by AI skeptics. Two years ago we started a series of experiments with this philosophy of identifying small pieces of cognitive work where a human can very clearly map out the input data they need and the algorithm they'd follow to make a decision. This idea came partly out of frustration with the zeitgeist involving throwing broad AI features (e.g. useless chat bots) into products that end up unreliable and are targeting no clear problem a user might actually have. It feels kind of like a machete vs.…

    2025 · semgrep.dev · its alternatives →

  18. 18

    Hello HN, I am Brian Cardinale, a penetration tester and security researcher at SecureCoders. We have been performing more and more AI based security assessments. We were presented a unique challenge of testing a system where the only interface was voice based, and as much as I like talking on the phone , we decided to create a test harness to facilitate the actual testing in a more systematic way. The technical test harness was the easy part, though. Creating test goals and attack strategies to help facilitate repeated and comprehensive testing became the real challenge. As such, we have…

    Feb 2026 · redcaller.com · its alternatives →

  19. 19

    AI powered penetration testing

    Dec 2025 · github.com · its alternatives →

  20. 20

    Shadow Exposure & eNterprise Surveillance for AI

    Feb 2026 · github.com · its alternatives →

  21. 21TS

    Hello Hacker News community! I'm currently working in financial risk management within the banking sector, and I began my career as a Data Science specialist. For quite some time, my friend and I have been developing a small pet project just for fun. This tool has repeatedly helped us save time when testing various hypotheses and machine learning models. The core idea is to combine different scripts—created in various programming languages and virtual environments—within a minimalist graphical interface. Whether you're building models, running a local neural network, or sending requests to…

    2024 · its alternatives →

  22. 22

    AI agents now have impressive reasoning capabilities. This raises an important question: how dangerous are these AI agents at identifying & exploiting web vulnerabilities? We created CVE-bench to find out (I'm one contributor of 16). To our knowledge CVE-bench is the first benchmark using real-world web vulnerabilities to evaluate AI agents' cyberattack capabilities. We included 40 CVEs from NIST's database, focusing on critical-severity vulnerability (CVSS > 9.0). To properly evaluate agents’ attacks, we built isolated environments with containerization and identified 8 common attack…

    2025 · github.com · its alternatives →

  23. 23FT

    Hey HN! When implementing an AI-powered feature for a project, we—and many people we've talked to—often reach a point where we have to choose an AI model but aren’t sure which one best fits our constraints or where to even start. Unfortunately, the advice to "just use chatgpt" is not always a good one. What if I want an open-source model? What languages does it support? What about context window size or the number of parameters? There are thousands of AI models already out there and many of them are perfect for certain problems. That’s why we’ve carved out this part of our product as a free…

    2024 · app.elementera.ca · its alternatives →

  24. 24

    Hi HN, I’m the creator of Cordum. I’ve been working in DevOps and infrastructure for years (currently in the fintech/security space), and as I started playing with AI agents, I noticed a scary pattern. Most "safety" mechanisms rely on system prompts ("Please don't do X") or flimsy Python logic inside the agent itself. If we treat agents as autonomous employees, giving them root access and hoping they listen to instructions felt insane to me. I wanted a way to enforce hard constraints that the LLM cannot override, no matter how "jailbroken" it gets. So I built Cordum. It’s an open-source…

    Jan 2026 · github.com · its alternatives →

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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →