Advertisement
Advertisement
⚡ Community Insights
Discussion Sentiment
79% Positive
Analyzed from 4485 words in the discussion.
Trending Topics
#security#codex#openai#https#com#more#scan#code#python#here
Discussion Sentiment
Analyzed from 4485 words in the discussion.
Trending Topics
Discussion (190 Comments)Read Original on HackerNews
Thanks for checking this out and for flagging the auth issues. We just open-sourced it, and there's still plenty for us to improve. Expect the product to evolve quickly.
If you try it, I'd really appreciate hearing what works well and what you think we should improve. Happy to answer questions here.
CLI docs: https://learn.chatgpt.com/docs/security/cli
EDIT: If you'd like to help make this better, we're hiring: https://openai.com/careers/full-stack-software-engineer-cybe...
I'm amazed that the requirements are so low (or at least this vague) for jobs at companies like these.
Has anyone else had the experience of going to an interview and feeling like you were never asked any qualifying questions?
All the questions were easy, your answers were straightforward, you "got them right", but then were not chosen?
I find on the other side, they're also left with dozens of people who "passed" and then it comes down to a pretty arbitrary decision on who gets hired (if we are talking external, no referral, etc.)
I wonder if they can make job descriptions highly specific to filter the shortlist faster and more effectively (to actually get a shortlist).
Anyway end rant. Cool job, hope you fill it.
Maybe this is part of the problem
It's not necessarily corruption. If there's no conflict between principal and agent, it's fine.
Just like when I sent my butler to go and buy a bottle of wine, he can make arbitrary choices, but that doesn't mean he's corrupt or going against my wishes. I trust his judgement, and since it's a repeated game our incentives are aligned.
At the start of my career I was under-credentialed, and had to rely on lax requirements to get myself in front of people. I might never have gotten anywhere if my first few employers hadn’t been willing to overlook a lack of degree or commercial (rather than open-source) experience, directly as a result of a funnel with a wide entrance.
Yes, many jurisdictions have outlawed arbitrary discrimination against protected classes (eg race), which is an entirely different matter, and not what we are discussing here.
Okay. This makes it sound like they're more sophisticated but it seems more like they are less sophisticated, less specific, and a lot more vague in the job descriptions they themselves create.
If you look at any technical role, game dev or something where people are building important things at scale - there are a lot of specifics. Libraries, methodologies, where if you didn't know them you are nowhere near a fit.
I'm just wondering. It's OpenAI. Surely there is some domain-specific something beyond "has experience shipping front-end and back-end services" since that includes basically everyone.
It makes this job look like a Starbucks role.
How does it deal with the current guardrails 5.6 Sol has on finding vulnerabilities? When I use it in the Codex app it would sometimes say it found a vulnerability, but it cannot tell me what it is.
For authorized defensive work, Trusted Access for Cyber (TAC1/Daybreak) can reduce refusals depending on the model and the account or organization where access is provisioned. It isn't a blanket bypass.
If you're an open-source maintainer, you can apply for conditional Codex Security access here:
https://openai.com/form/codex-for-oss/
For enterprise teams, the public Daybreak onboarding guide is here:
https://help.openai.com/en/articles/20001261-enterprise-dayb...
If you have an example of "found a vulnerability but won't tell me what it is," I'd love to take a look too. You can send it to use with /feedback (or message me).
Or perhaps a better option is to use something like Kimi K3 and cancel the GPT subscription altogether.
> https://openai.com/form/codex-for-oss/
Hey, Lead maintainer of vim here. Applied twice already never heard anything back. This is a frustrating experience!
I think it would be a good practice to refund the session cost in that case. Otherwise a customer just spent some money in order to get exactly nothing.
It said "Partial output was kept at <...>", but I dont see a obvious way of picking it up in a new scan? (The failed run cost me ~$13)
We've been talking to hundreds of engineering and security teams, and their feedback is shaping what we build.
Like Promptfoo, our goal is practical tooling that fits into the workflows teams already have.
Really glad we got to ship this, and there's still a lot we want to improve in Codex Security and in Promptfoo!
> Thanks for checking this out and for flagging the auth issues.
Offtopic, but this right here is why I don't believe any marketing around "great amazing models that one-shot everything and programmers are no longer needed".
You just have to look at what these labs routinely produce, and their own products.
Edit to respond to @simonw whose comment I saw before he retracted it ;)
This comment is tied directly to consistent continuous claims by the LLM labs. Their own products disprove their own claims, and it would indeed be nice if fewer people believed them :)
Promotions all around.
For context can you share the line count?
If ya wouldn't mind crediting me a quick 60 months that would be great.
Can’t speak to the results, but the cost isn’t high.
I've been noticing that many new projects that would have been written in Python or Node a year ago are starting to be written in Go, Rust, etc.
Theory: people realized there’s little benefit to Python for agents. As Zep wrote, an “agent is a long-running, concurrent, I/O-bound process that spends most of its time waiting on a model, a tool, or a human[1]” — not a particular strength of Python.
I'm wondering if you'd considered Go (or others—Go’s just my fav ) before landing on Node, and more broadly whether you've noticed a similar pattern?
1: https://blog.getzep.com/agentic-development-in-go/
That sounds exactly like a strength of Python, no? Python is excellent at working IO blocks and waiting in general being interpreted language with first-class async support.
s/
I generally don't write Python, but like others, I disagree with GP too. In fact, a lot of my work involves Python being written now, simply because that's what LLMs like to write.
I can’t fathom why anybody would want to continue working with dynamically typed languages when they can now get types for free.
Golang/rust however are very convenient to distribute. Small, portable, fast exe's are very nice. With agentic coding golang/rust are now accessible to a lot more people.
I see where you're coming from, but to be honest I respectfully disagree. If all you mean to do is writing small one-off scripts then sure Python may be the right tool[1], but when you're doing something more complicated Go is just simpler. And for an LLM, Go is even better for all the reasons mentioned in sibling comments and OP.
[1]: I tend to rely more on Bash, though...
please tell me you're reading the AI code
Some of approaches there could be useful in other contexts. OAI has the compute to experiment with different prompts and I'd expect these to be somewhat optimized.
https://x.com/openai/status/2082263717916586117?s=46&t=mnfnj...
I’m confused. Why are you thanking them for that?
codex-security scan . [00:00] Preparing scan [00:00] Authentication: stored Codex credentials. [00:01] Preparing scan [31:25] Running scan codex-security: This content was flagged for possible cybersecurity risk. If this seems wrong, try rephrasing your request. To get authorized for security work, join the Trusted Access for Cyber program: https://chatgpt.com/cyber
I mean... wasn't this the intended goal? And you wasted all my tokens for nothing?!
Can they explain what types of projects it works on and how does it check I own it? Like will it just not work on Linux kernel even on my own patches to it?
The CLI doesn't do a repository-ownership check. Public projects are supported, and reviewing your own Linux kernel patches is the kind of defensive work we want to support.
The refusals come from model guardrails, which can be overly cautious. Trusted Access for Cyber (TAC1/Daybreak) is a separate, approved access path that can reduce those refusals.
If you're an open-source maintainer, you can apply for conditional Codex Security access here: https://openai.com/form/codex-for-oss/
For enterprise teams, the Daybreak onboarding process is explained here: https://help.openai.com/en/articles/20001261-enterprise-dayb...
If you have a specific repro, I'd be happy to look into it.
Getting into the Cyber program seems like a hassle as a freelance/open source person with tiny projects. Think used in production at 2-3 companies but only 20 something stars(ofc I don't market it but it just feels very unfair).
https://github.com/alibaba/open-code-review
For API, Business, and Enterprise accounts, business data isn't used to train models by default. Retention and other data controls depend on the product and account configuration.
If your company doesn't allow source code to leave its environment, you shouldn't run this against that codebase. Local and third-party endpoints aren't officially supported yet, but you can read through the code and your favorite coding agent will allow you to use it with any model of your choice in 30 seconds.
More on OpenAI's enterprise data handling: https://openai.com/enterprise-privacy/
This is true and has to be true for any hosted model that works with existing code: it's not specific to this application.
How can I trust that they show me all findings they have instead of selling the best ones to some three letter organisations?
Also they haven't figured out codeberg exists, so the resume page of a project of mine on snyk[1] still links to github and reports the project as "inactive", having the last commit 2 years ago, and the last release 2 months ago. I think it's quite telling of their quality.
1. https://security.snyk.io/package/pip/typedload
https://github.com/openai/codex-security/pull/22
One thing worth checking in the meantime: OPENAI_API_KEY or CODEX_API_KEY can override an existing ChatGPT/Codex login. If you're trying to use your ChatGPT login, run this in bash or zsh:
Then retry your scan.If it still fails, could you share the exact error and whether you're using ChatGPT login or an API key? Happy to help debug. You can also file an issue in the repo and we'll take a look!
Edit: there's a little bit more meat here: https://github.com/openai/codex-security/tree/main/sdk/types...
Just a few days back, I was reviewing some small bit of legacy DSA signature verification code, to get a sense of how safe it is to reuse - purely defensive, precautionary work and the context of it was there. But I simply wasn't able to use Codex Security: it threw refusal tantrums on every step of the way. Even the reasoning went like "nah, this is false positive, this is defensive code hardening, I'll nuke the subagent and tell it so" , followed by a refusal.
In the end, I was only able to do partial review with vanilla Codex w/o Codex Security.
https://wasnotwas.com/writing/inside-openai-codex-security/
https://pornoscan.us/codex-security-analysis.html
I'm building AQ, a coding harness for teams and the pattern is identical. For a while, I thought the raw model is the answer and quickly changed my mind. Purpose built harnesses are way more powerful than it sounds.
"If we had only used this tool, OpenAI would've had to pay off another patsy for their marketing stunt" -- Huggingface
"The 'S' in OpenAI is for "Security". Ever since we developed this tool we've had almost zero AI generated reports of outbreaks of allegedly-rogue AI agents breaking out of allegedly-secure testing environments, probably" -- OpenAI