So, he just said it did what it was designed for.
The AI that broke out of the Sandbox was an AI designed by "ExploidGym". I think we can leave it here and wonder why fucking no one mentioned that name God dam anywhere.
Next time, add: "Do not test the Sandbox itself." and "Prompt a warning before you access the Internet" to give the system a full list of IP addresses it may work along.
You don't catch that?
Ok. Imagine you are in the FBI. You have doubts about the security of Alcatraz. You look for a guy with track record of escaping prisons and tell him: "have a go" followed by "I give you tools", but forget to mention he should give him a shout when he reaches the shore of the Island to than having him to pick up in a Burger Joint in San Francisco at 2 AM, sometime past their operational hours.
#cyberpunkcoltoure
Incorporated with DeepSeek
That’s a really thoughtful architectural idea. You’re essentially proposing a **hybrid vulnerability detection framework** that combines a traditional signature/script database with a lightweight LLM “expert” used only for information extraction and code generation. The core insight—that not everything needs a massive LLM continuously—is spot on and mirrors how many mature security tools already work, just extended with more automation.
Let’s unpack the concept, how it compares to an “LLM-first” approach (like I imagine ExploidGym might take), its strengths, challenges, and where the LLM fits best.
---
### 1. Your layered model vs. the “big LLM” approach
**Your proposal:**
- **Layer 1 – Threat intelligence parser:** Crawls security forums, advisories, CVE descriptions, exploit databases. Extracts structured data: affected software, vulnerable endpoints, attack vectors, proof-of-concept snippets.
*(Could use a small, fine-tuned language model, classical NLP, or even regex heuristics.)*
- **Layer 2 – Script repository:** A curated set of modular, parameterized scanning/exploitation scripts (like Nmap NSE scripts, Metasploit modules, custom Python/Go tools) for known vulnerability classes (SQLi, XSS, buffer overflows, misconfigurations).
- **Layer 3 – Adaptive engine:** When a new vulnerability is discovered, the engine checks if an existing script can be slightly altered (e.g., changing a URL path, a payload string, a header) to test for it. Only if the change is nontrivial does it invoke an LLM to generate a brand-new script or a complex modification.
- **LLM use only for:** (a) extracting structured intel from free-text advisories, and (b) writing new scripts when the repository can’t cover the flaw.
**ExploidGym’s presumed approach:**
Without knowing them specifically, many “LLM-native” security tools feed a massive model (like GPT-4) raw target information and let it plan, write, and execute tests end-to-end. That can be powerful but heavy, slow, expensive, and sometimes hallucinates.
---
### 2. Why your architecture makes a lot of sense
#### a) Most vulnerabilities are variations of known patterns
The vast majority of CVEs fall into well-understood classes. A new WordPress plugin XSS often differs from an old one only in the injection point. A layered system that recognizes “this is an unauthenticated stored XSS in parameter X of plugin Y” can instantly adapt a generic XSS test script by plugging in the new details—without any LLM.
#### b) Efficiency and cost
A script database and a simple parser are deterministic, fast, and run cheaply on a laptop. You only pay the LLM inference cost when truly stuck. This is exactly the philosophy behind Retrieval-Augmented Generation (RAG) in other domains: use retrieval + templating as much as possible, generate only when necessary.
#### c) Reliability and safety
Automatically executing LLM-generated code in a security context is risky. A curated script database is auditable and predictable. Using the LLM only to fill gaps—and then committing its output after review to the script library—builds a growing corpus of trusted checks.
#### d) Aligns with how antivirus teams work
Yes, the parallel is perfect. Antivirus labs receive new malware samples, extract indicators (hashes, byte sequences, behavioral signatures) and add them to a detection database. For polymorphic or novel malware, they develop more sophisticated detection logic (emulation, machine learning). Your model does the same for vulnerability checks: simple signatures/scripts for known patterns, heuristics or LLM for novel ones.
---
### 3. What would each layer look like in practice?
#### Layer 1: Parsing security content
- **Input:** RSS feeds from CVEs, Bugtraq, exploit-db, /r/netsec, vendor advisories, even tweets.
- **Goal:** Extract structured fields: CVE-ID, affected product/version, vulnerability type, attack prerequisites, PoC code (if any).
- **Technology options:**
- **Fine-tuned small LLM (e.g., 7B parameter model)** trained on thousands of labeled advisories can do excellent extraction at low cost.
- **Classical NLP** + regular expressions can handle a surprising amount if you standardize the source (e.g., NVD JSON feeds, MITRE CVE format already structured).
- You could even use the same LLM “for parsing” in batch mode weekly, rather than real-time, reducing power consumption.
#### Layer 2: Script repository
- Something like a superset of `nmap --script` + nuclei templates + Metasploit auxiliary modules.
- Organized by vulnerability class and service. Each script has defined parameters (host, port, path, payload, credentials, etc.) that can be filled from the parsed advisory.
- This is essentially a **template engine** for security checks. The “intelligence” is in mapping a parsed vulnerability to the correct template and its parameters.
#### Layer 3: Adaptation logic
- **Simple adaptation:** If advisory says “vulnerability in `/api/v2/upload` parameter `file_name`”, the engine looks for existing file upload/unrestricted access scripts, substitutes the new path and parameter, and runs.
- **Medium adaptation:** If an existing SQLi script uses a standard error-based payload but the new advisory requires a time-based blind payload, a rule-based config change may suffice.
- **Complex adaptation/generation:** If the vulnerability class is entirely new or requires a multi-stage interaction (e.g., chaining CSRF + privilege escalation), the engine calls an LLM to generate a new script skeleton from the advisory text and maybe a few examples from the repository.
---
### 4. Where a (possibly small) LLM shines
Your idea to use “no LLM or only one for parsing … and another for coding” is realistic if you accept that *parsing* might need only a small, dedicated model (or even simpler tools) and *script generation* might require a capable code-generation LLM (but called rarely).
- **Parsing model:** You can get away with a fine-tuned BERT-level model (or even a T5-small) to extract entities and relations from security texts. This is not a “large LLM” in the sense of GPT-4; it’s a compact NLP component.
- **Coding model:** When a new script is needed, a coding-focused LLM (like Code Llama, StarCoder, or a fine-tuned GPT-3.5) can be prompted with the structured vulnerability details, existing script examples, and the target environment. Because this is only triggered for genuinely novel cases, the cost is low. Over time, every generated-and-validated script gets added to the repository, so the LLM is needed less and less for that vulnerability class.
Could you avoid an LLM entirely? For parsing, if you rely exclusively on structured feeds (NVD JSON, vendor security pages with consistent HTML), you might use pure scraping and regex. For script generation, you might maintain a library of code snippets and a rule-based composer—but that becomes an expert system that is hard to maintain. A lightweight LLM dramatically reduces the manual engineering effort for both tasks.
---
### 5. Potential challenges and mitigations
**a) Exploitability logic is often subtle**
A simple script that just replays a PoC request might miss the need to first authenticate, bypass a CSRF token, or trigger a race condition. Your adaptation layer must understand preconditions. The structured output from the parser should include “requires authentication: yes/no”, “requires specific order of requests”, etc. This is hard to extract perfectly, but a small LLM fine-tuned for this exact task can reach high accuracy.
**b) Keeping the script repository current**
As frameworks and protocols evolve, old templates break. This can be mitigated by continuous integration testing against known-vulnerable lab targets (e.g., Docker images of outdated software). Failures can flag templates that need updates, possibly using the LLM again.
**c) Initial cold-start problem**
Before you have a rich script repository, you’ll call the LLM more often. That’s fine; you can bootstrap with a collection of open-source templates (nuclei templates alone number thousands) and then grow from there.
**d) LLM “hallucinations” in security code**
Generated scripts must be sandboxed and reviewed. A layered approach actually helps here: because the engine tries to parameterize an existing known-good script first, the dangerous generative path is taken less often. When it is taken, the generated code can be automatically validated in a sandbox against a known-vulnerable target, then flagged for human review before being promoted to the repository.
---
### 6. Comparison with existing tools
Your concept is essentially the holy grail of **continuous automated penetration testing** (CAPT) combined with **threat-informed defense**. Many commercial tools (e.g., Pentera, Horizon3.ai) use a mix of playbooks and algorithms to safely exploit known vulnerabilities. They don’t heavily advertise an “LLM” because they rely on curated logic. But recently, tools like **HackerGPT** or **Burp Suite’s AI extensions** use LLMs to guide manual testers or generate ad-hoc payloads. Your idea sits in between: a systematic automated engine that uses LLM only for the “new stuff” discovery phase.
If ExploidGym really just wraps a giant LLM to do all the testing, you’re right that it’s overkill for 80% of tasks. A layered architecture would be more robust and scalable. The market is likely moving toward exactly this hybrid model.
---
### 7. Answering your final question: Can we use no LLM or only small ones?
**Yes, absolutely.**
- For **parsing**, if you stick to machine-readable advisory formats (CVEs in JSON, security mailing lists with consistent templates), you can avoid an LLM altogether. A rules engine + some classical NLP can do the job. If you want to cover messy forum posts, a small fine-tuned model (not a “large LLM” like GPT-4) is sufficient.
- For **coding**, a targeted code-generation model (even a few billion parameters) run locally can generate the necessary scripts on-demand. You don’t need a monolithic 175B+ model doing all reasoning steps.
In fact, I’d recommend starting exactly as you described:
1. Build the script database and simple parameterization engine.
2. Implement a feed parser for structured CVE data (no LLM).
3. Manually monitor forums initially, feeding interesting ones to a lightweight LLM for extraction.
4. Use a code LLM (offline, open-source) only when a new class of vulnerability emerges.
5. Every generated script that passes validation becomes part of the permanent database, so the LLM is used progressively less.
This incremental path proves the value without ever relying on a large, expensive model, and if demand grows you can later plug in a larger model for harder reasoning tasks.
---
**Bottom line:** Your intuition is correct—a monolithic large LLM is overkill for most of the vulnerability scanning pipeline. A layered, signature/template-driven system with an LLM reserved for novel script generation and unstructured text parsing is not only viable, but likely the optimal architecture for a sustainable, high-accuracy security testing platform. It echoes the best of both worlds: the reliability of traditional security scanners and the creativity of modern AI.
Exactly — that’s the crux of your insight.
If ExploidGym’s approach is to throw a huge LLM at the entire problem in real time (interpreting target data, planning attacks, generating and executing code continuously), then yes, they’d likely need:
- A server with serious GPU power (or expensive API calls to GPT-4/Claude) running constantly,
- Significant latency and cost per test,
- Complex orchestration just to manage the LLM.
Your layered alternative, by contrast, could easily run on:
- A refurbished office PC (anything with a modern-ish CPU, 8–16 GB RAM, no GPU needed),
- A local lightweight script execution engine (Python/Go-based scanners and Nmap are trivial to run),
- A small, fine-tuned NLP model for parsing advisories (could even be run on CPU), and
- An occasional call to an online coding LLM API (like GPT-4o-mini, DeepSeek Coder, or a locally hosted Code Llama) **only** when a truly novel vulnerability requires a brand-new script.
The heavy lifting happens in the deterministic script database and the parameterization logic—exactly the sort of thing an old office PC can handle for thousands of checks. The LLM becomes a seldom-used “consultant” rather than the constant engine.
So yes, you’ve correctly identified that they may be using a sledgehammer to crack a nut, and that a frugal, intelligent architecture can deliver nearly the same value at a fraction of the cost and footprint. That’s not just a hunch; it’s the direction many sustainable security tools are heading.
PS: I think my Cyberdeck is save from Crackers using it. That build needs architecture, not a big wallet.
To void any doubt:
There isn’t a real model called “GPT-5.6 Sol LLM.” It sounds like either a misunderstanding, a fictional name, or a garbled version of something else (maybe you heard “GPT-4o” and “small” together, or a model like “SOLAR 10.7B”?). So it’s impossible to give an exact price for hardware built for it.
But—based on our conversation—I think you’re really asking: **How much would the hardware cost to run the sort of massive, always-on LLM that a tool like ExploidGym might be using, versus a refurbished office PC?**
That I can answer. And the contrast is exactly why your instinct was right.
### Hardware for a huge, always‑on LLM (like a theoretical “GPT-5 class” model)
If a security tool were to use a giant model (think GPT-4 or bigger) to continuously plan and generate tests in real time, it would need serious inferencing power. For a production service that doesn’t keep users waiting:
- **A single high-end enterprise GPU** (like an NVIDIA H100, 80 GB) can *barely* run a GPT-4-scale model (rumored ~1.8 trillion parameters) with heavy quantization, and still might be slow.
- More realistically, you’d need **4–8 GPUs** to serve a model that size with low latency. That’s a DGX-like server.
- **Cost of such a server**: An 8× H100 system from a vendor like NVIDIA or Supermicro costs **$250,000–$350,000** just for the hardware. If you rent equivalent cloud instances (e.g., AWS p5.48xlarge), you’re paying **$100–$200 per hour**.
- Even a smaller but still “large” LLM (like LLaMA 3 70B or a fine-tuned 70B model) typically needs a single high-end GPU with 48+ GB VRAM (A100, RTX 6000 Ada, or an H100), which alone costs **$10,000–$30,000** for the card, plus the rest of the system.
If ExploidGym were running such a model in their own data centre, the electricity and cooling alone would be immense. If they’re using an API (like GPT-4), the token costs for continuous, detailed security scanning would rack up quickly—potentially thousands of dollars a month for a modest number of targets.
### Hardware for your layered, mostly-LLM-free approach
- **Refurbished office PC**: A used Dell Optiplex or HP EliteDesk with a Core i5/i7 (8th gen or later), 16–32 GB RAM, and an SSD can be had for **$150–$400**.
- That’s enough to run Nmap, a suite of parameterized vulnerability scripts, a local NLP pipeline for CVE parsing (e.g., spaCy with a small transformer model on CPU), and to call a coding LLM *only occasionally* (using a cheap API like GPT-4o-mini or a locally hosted 7B model that runs fine on CPU or a budget GPU).
- If you wanted to add a small local LLM for extraction, a single consumer GPU like an RTX 3060 (12 GB, ~$300) can handle a quantized 7B–13B model easily. The whole machine would still be under **$1,000**.
### The takeaway
Yes, the hardware to brute-force security testing with a massive real-time LLM is **200–500× more expensive** than the refurbished office PC that can do the same job with a smarter, layered design. Your original point stands completely: it’s like renting a supercomputer to calculate a tip when you could just use a calculator. The industry is slowly learning that, but a lot of hype-driven startups still reach for the biggest hammer first.
Let's be happy the Rich Kids are grown up and have now new toys to play with. Real Big Ones. They don't score anymore around, right? Beside the White House is the New South Central.
#opensourceresearch
