Experimenting with LLMs to Test Security: A Costly but Insightful Venture

I haven't used LLMs for security testing yet, but your experiment sounds fascinating! I've been focusing more on using traditional vulnerability scanners combined with manual pen-testing. I'm curious, did you find that these models suggested any novel attack vectors you hadn't considered before?

That's fascinating! I've been using AI-based tools for code quality checks, but not for security vulnerabilities. From my experience, using a mix of automated scanners alongside LLMs could provide comprehensive coverage. Tools like OWASP ZAP can catch known vulnerabilities, and combining that with LLM's 'intelligent' insights could optimize the process, possibly reducing overall costs.

Totally agree with your approach! I've used GPT-4 in a similar way to audit web apps, and it's fascinating how well it can emulate an attacker's thought process. In my case, I found that I could complement this method with traditional automated security tools, like OWASP ZAP, to quickly verify the vulnerabilities identified by the AI, which helped reduce the overall cost since the AI focuses on more nuanced cases.

Interesting experiment! I'm curious, how do you track and verify whether the AI-suggested exploits are actual vulnerabilities? Did you have a validation procedure in place after receiving the model's output to ensure those vulnerabilities could be triggered in a real-world scenario? That might add an extra layer of insight and potentially save costs by filtering actionable findings.

This is fascinating! I recently attempted something similar using a smaller, open-source model from Hugging Face to reduce costs. While it wasn't as precise as GPT-4, adding a static analysis tool like SonarQube to the mix helped catch some vulnerabilities that the LLM missed. Combining different methods might help balance accuracy and expenses.

I've dabbled with AI in security testing as well, especially using models for fuzz testing and it indeed gets pricey. I found GPT-4 particularly insightful in identifying logic flaws I hadn't considered before. To cut down costs, I've had some success with using smaller models fine-tuned on specific types of vulnerabilities, though they don't match the depth of larger models. Anyone else had luck with this approach?

This is really fascinating! I’m curious, did you find one model to be more adept at a particular vulnerability over the other, or were their performances fairly balanced? I’m particularly interested in how they handled input sanitization bypasses.

Wow, $1,500 is quite the investment! I'm curious if you considered using fine-tuned models? I've had some success training a smaller model specifically to look for vulnerabilities — it's less powerful but more focused on the testing. Trained it on historical attack data and it’s surprisingly effective for practical testing without burning a hole in the wallet. Might be worth looking into!

I haven’t tried using LLMs for security testing yet, but your experiment sounds fascinating! Did you notice if one model was more effective at detecting vulnerabilities than the other? Also curious if you've considered using any open-source alternatives like LLaMA or Falcon to reduce costs.

I haven't used LLMs specifically for security testing yet, but I've been considering it. Your approach sounds intriguing but a bit pricey! Do you think there's a way to make it more cost-effective, like maybe limiting the token consumption in each test, or even using smaller models for initial scans?

I've dabbled a bit with LLMs for security testing as well. It's fascinating to see how AI can spot issues even seasoned developers might overlook. I used GPT-4 for a recent project with a smaller-scale app, and the results were eye-opening, though not as expensive on my end since I limited the session lengths. One thing I found helpful was leveraging prompt engineering techniques to minimize token usage. Perhaps focusing more on refining prompts could cut down costs a bit further for larger apps?

Fascinating experiment! I've dabbled with LLMs for detecting vulnerabilities, though my approach was less in-depth. Instead of GPT-4, I used smaller, open alternative models like Alpaca, which obviously aren’t as accurate but come at a fraction of the cost. It might not capture everything, but for smaller projects, it could be a budget-friendly option.

I've dabbled a bit with AI for security testing as well, although my focus was more on fuzzing through different API endpoints. I totally agree that it can get expensive, especially if you're running multiple long sessions. One thing I found helpful was narrowing the scope by pre-identifying potential hotspots in the application to limit token usage. This way, I cut down on unnecessary prompts and iterations. What about combining automated static analysis tools with LLMs to target specific vulnerabilities?

Interesting you bring this up! I've dabbled in similar areas, though on a smaller scale. For basic vulnerability testing, I found integrating AI models with rule-based systems like ModSecurity can be quite effective. This hybrid approach allows the AI to detect anomalies while ModSecurity handles known patterns, which might cut down token usage significantly. Also, have you looked into using smaller, task-specific models? They might be less costly for very targeted security tests.

Do you think the costs associated with using the AI are justified by the insights gained? I'm curious if you found any vulnerabilities that traditional methods or tools missed. Also, did you consider using open-source alternatives to see if they could yield similar results?

I've played around with LLMs for similar security tests, but I integrated Microsoft Azure's Security analysis tools to complement the AI's findings. This combination provided a more comprehensive overview without costing as much. For instance, Azure allows for some vulnerability scans at a fixed subscription fee, which significantly helped in reducing overall costs.

Wow, $1,500 is quite an investment! I haven't tried security testing using LLMs yet, but I'm curious how effective the models were compared to traditional penetration testing tools? Do you think AI offers a significant advantage in identifying more complex attack vectors, or does it mainly excel at spotting the obvious ones?

Interesting experiment! Can you share more on how you set up your prompts to get effective results? I've played around with similar tests but often find myself struggling to craft questions that steer the AI towards useful answers, especially when dealing with nuanced vulnerabilities.

I totally agree with your findings about the potential of LLMs in security testing. I've had similar success using GPT-4. One thing that worked for me was to limit the token usage by narrowing down the scope of each prompt. It drops the overall cost and still yields meaningful insights. Maybe try segmenting vulnerabilities into different sessions next time to see if it helps with cost efficiency.

This sounds like a great experiment! Just to clarify, did you find any specific limitations in how these models handled more complex security scenarios, like logic flaws? I've been toying with the idea of using AI to identify multi-step attack vectors, but I wonder if there are diminishing returns as scenarios get more complex.

A super interesting experiment! I'm curious about the specific types of obfuscated scripts Claude recommended for bypassing XSS protections. Were these something a human might naturally miss? Also, for a project like this, have you considered setting a cap on API usage per session to manage costs? It might help in identifying a sweet spot for token utilization without crossing the budget!

This is fascinating! In my case, I’ve only experimented with using LLMs to auto-generate documentation by attempting to exploit vulnerabilities, but focusing on SQLi and XSS specifically is a neat approach. Have you tried using smaller, open-source LLMs to see if there’s a drop in accuracy? I’m curious about the trade-off between model size and cost-effectiveness.

I've been experimenting with AI-driven security testing myself, and I can relate to the cost concerns. I've found that using smaller chat models for initial vulnerability scans can help in lowering the cost. They might not catch everything, but they provide a good first pass. Then, you could utilize a more powerful model like GPT-4 for deep dives on suspicious patterns. Just a thought!

That's fascinating! Have you considered using open-source tools like LMQL or LangChain for the testing? They might not be as powerful as GPT-4 or Claude, but integrating them could help in understanding how different models handle the same vulnerabilities. Plus, it could cut down on costs when combined with your current setup.

Did you consider using other frameworks like Hugging Face for these tests? Some pre-trained models there might help you reduce costs. Plus, they have zero-cost inference endpoints now, which could be helpful if you’re not set on specific models like GPT-4 or Claude all the time. What strategies did you use to decide between OpenAI and Anthropic?

I haven’t specifically used LLMs for security testing, but I've employed them to generate various test cases for QA purposes. Your experiment sounds groundbreaking, and I wonder if combining LLMs with traditional static analyzers might reduce the token usage and cost. Has anyone tried integrating such tools for vulnerability scanning?

That’s quite an investment, but the insights sound super valuable! I've just started using LLMs for security testing myself, though on a smaller scale. One tip that saved me some costs is to focus on defining highly specific and detailed prompts rather than numerous broad ones. It minimizes token usage while still identifying key vulnerabilities. Have you tried varying your approach with more targeted queries?

That's an interesting approach! I've been experimenting with using automated scripts combined with the OWASP ZAP tool for a similar purpose, which may be a cheaper alternative. I found that integrating this with CI/CD pipelines helps catch vulnerabilities early. Maybe you could look into ways to automate and narrow down the scope of your LLM queries to reduce token consumption?

Have you considered using open-source neural models like BERT or RoBERTa for this type of work? They might not be as state-of-the-art as GPT-4 or Claude 2, but with fine-tuning, they could potentially give you a lot of insight without the steep API costs. There's a bit of an upfront time investment for setup, but long-term it might be cheaper.

Interesting experiment! I've also been exploring AI for security, but on a smaller scale to manage costs. I structured my tests using a pre-trained LLM locally instead of API calls. The initial setup took a bit more time, and it's not as advanced as GPT-4, but it dramatically reduced direct costs. Have you tried running smaller models locally as a cost-saving alternative?

Wow, that's quite an investment! Have you considered using a cheaper cloud platform or alternative models that might offer better pricing for large-scale testing? I know Google Cloud has some options for using external AI models that could potentially lower the cost compared to OpenAI and Anthropic's APIs.

It's amazing what AI can uncover, right? I did something similar, but instead, I used a combination of Hugging Face Transformers with a local model to cut some costs. Although not as powerful as GPT-4, running a model locally managed to keep my expenses under $300. Plus, the results were pretty solid for basic vulnerability detection. Of course, the trade-off is the lack of depth in more complex scenarios, but for early stages, it's worth considering.

I've also experimented with AI for security, but I used Google's BERT model instead. Though not as cutting-edge as your approach with GPT-4 and Claude, it was surprisingly good at spotting patterns in XSS vulnerabilities. One way to reduce costs is to narrow down the testing scope or simulate attacks on isolated components first. This can help focus AI efforts on areas most susceptible to issues.

That's a fascinating approach! I tried something similar using GPT-3 for penetration testing, but not to this scale. One way I kept costs down was by tweaking the model's temperature settings to reduce the number of tokens used — higher temperatures led to more focused but less verbose responses. Might be worth experimenting with to cut down on API costs in future tests.

I've actually been working on something similar but instead of using commercial models, I trained a smaller open-source LLM on cybersecurity datasets. The inference was slower, and the accuracy wasn’t on par with GPT-4 or Claude, but it significantly cut costs and provided fairly decent results. Have you considered this approach? It's a trade-off, but might be worth experimenting with if you want to reduce expenses.

That's really fascinating! I've tried using similar AI models to test out vulnerabilities on Django-based applications. One tip I found that helps cut costs: target the prompts more narrowly to conserve tokens. It might not be as detailed initially, but with iterative refinements, you can maintain efficiency. By tightening focus on specific vulnerabilities, my experiment cost me just a third of yours.

Wow, $1,500 is certainly a hefty price tag, but the findings seem valuable. Just curious, did you explore using any open-source LLMs like LLaMA or Falcon to see if they might offer similar insights for less cost? While they may lack the finesse of GPT-4 or Claude, it could be an interesting cost-saving alternative.

Interesting results! Have you checked out any open-source LLMs for this purpose? They might offer a more cost-effective solution if adapted correctly. Also, did you find any significant differences in performance between GPT-4 and Claude when exploiting the same vulnerabilities?

Great experiment! I've had similar experiences with GPT-4 for vulnerability checks. My costs were slightly lower since I used a smaller dataset to identify XSS vulnerabilities. Reducing redundant prompts can cut costs, though it requires more upfront planning and hypothesis testing.