AISLE Discovers 38 CVEs in OpenEMR Healthcare Software
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Looks like every single one of the 38 vulnerabilities were either SQL injection, XSS, path traversal or "Insecure Direct Object Reference" aka failing to check the caller was allowed to access the record.
This is actually a pretty good example of the value of AI security scanners - even really strong development teams still occasionally let bugs like this slip through, having an AI scanner that can spot them feels worthwhile to me.
if an AI uses static analyzers to do work ,is it the tool or the ai ?
if AI is using grep to do the work, is it the AI or grep?
I mean essentially all agent work boils down to "cat or grep?"
One would think a single really strong developer, let alone a team, would look for interpolation in strings fed to RDBMS?
Classic.
Seems like code review against a checklist of the most common vulnerabilities would have prevented these problems. So I guess there are two takeaways here:
First, AI scanners are useful for catching security problems your team has overlooked.
Second, maintaining a checklist of the most-common vulnerabilities and using it during code review is likely to not only prevent most of the problems that AI is likely to catch, but also show your development team many of their security blind spots at review time and teach them how to light those areas. That is, the team learns how to avoid creating those security mistakes in the first place.
But, yes, I'd augment any manual review with a checklist and AI review as a final step. If the AI catches any problems then, your reviewers will be primed to think about why they overlooked them.
Could not agree any more strongly. These automagic tools are one thing in the hands of a dev that groks the basics like these examples. It would be one thing if new devs were actually reviewing the generated code to understand it, but so much is just vibe coded and deployed as soon as it "works". I get flack from not immediately deploying generated code because I want to take time to understand how it works. It's really grating and a lot of friction is coming from it.
https://chatgpt.com/share/69f10515-8808-83ea-abe3-a758d3144c...
If people aren't learning more with AI, that's a meta skill they need to develop.
As for training the review muscles, why would you do that if you have a linter that rejects when you make the mistake? I don't expect reviewers to check whether you eschew nulls or uninitialized variables; I expect the compiler to do that, and I expect over time that more and more things will become tooling concerns (especially given that rigid tools with appropriate feedback are clearly a massive force multiplier for LLMs).
Second, to use your example, the ChatGPT response you provided does a crappy job of explaining the root cause of problem: Namely, that every string is drawn from some underlying language that gives the string its meaning, and therefore when strings of different languages are combined, the result can cause a string drawn from one language to be interepreted as if it were drawn from another and, consequently, be given an unintended meaning.
So, if the idea is that smart teams can not only delegate the catching of problems but also the explanation of those problems to ChatGPT -- presumably because it is a better teacher than the senior engineers who actually understand the salient concepts -- I'd say AI ain't there yet.
Is that true? Is that also true of e.g. teams using type checkers to avoid nulls or exceptions? Or teams that use memory safe languages to avoid memory corruption? Or using a library that has an `unsafeStringToSql` API surface, and a linter to flag its use (where you're expected to use safe macros instead)? My experience is that better tools (or languages and library designs) scanning for issues lead to fewer defects and less playing fast and loose since the entire point of the tools is to ban these mistakes.
On education, it literally tells you that the top concern is SQL injection made possible by concatenating strings, and gives an example of an auth bypass: `name = "foo' OR 1=1 --"`. It also notes that this is not just a minor nitpick, but that actually the solution is fundamentally doing something completely different (query objects with bound parameters). If you don't understand what it means you can just ask:
> Elaborate on 1
> Walk through examples of what goes wrong and why, and how the solution avoids it
etc. The knowledge is all there; you just need to ask for it. It's an infinitely patient teacher with infinite available attention to give to you. You can keep asking follow-ups, ask it to check your understanding, etc. Or there are tons of materials about it on the web or in textbooks, and if you still don't understand, you can still ask a more senior engineer to explain what's wrong.
Yes. See: vibe coding. See also: The shockingly widespread hype for and acceptance of vibe coding across industries that ought to know better.
Do you deny that there is a correlation between AI use and not knowing what you are doing? Isn’t one of the big selling points of AI is that it lets “regular people” create “real world” projects that they could only dream about previously?
I am not saying that serious engineers don’t use AI or that when they use it, they do so foolishly. I’m only pointing out that AI has let a lot of people who don’t know what they’re doing crank out code without understanding how it works (or doesn’t).
> Is that also true of e.g. teams using type checkers to avoid nulls or exceptions? Or teams that use memory safe languages to avoid memory corruption?
No, it is not true of those teams. When people choose to use languages with statically checked types or with memory safety or the other examples you offered, they are rarely doing it because they have no idea how to write sound code. But when people turn to AI to crank out code they couldn’t write themselves (see: vibe coding), that’s what they are doing.
> On education, [ChatGPT] literally tells you that the top concern is SQL injection from essentially concatenating strings, and gives an example of an auth bypass: `name = "foo' OR 1=1 --"`. If you don't understand what that means you can just ask...
Again, that’s a crappy explanation of the real problem. It promotes no understanding of the underlying issue—that strings are drawn from languages that give them their meanings. And, unless you understand that it’s a crappy explanation that ignores the underlying issue—which a person being gaslit by the crappy explanation would not—what stimulus is going to provoke you to ask for a better explanation? How are you going to know that the crappy explanation is crappy and tell ChatGPT to take another direction?
> The knowledge is all there; you just need to ask for it. It's an infinitely patient teacher with infinite available attention to give to you.
Yeah, and if it steers you down a crappy path, such as in your sql-injection session with ChatGPT, it will be infinitely happy to keep leading you down that crappy path. Unless you know that it’s leading you down a crappy path, you won’t be able to tell it to stop and take another path. But if you are relying on the AI to tell you what’s good and what’s crappy, you won’t be able to tell which is which. You’ll be stuck on whatever path the AI first presents to you.
> Or there are tons of materials about it on the web or in textbooks, and if you still don't understand, you can still ask a more senior engineer to explain what's wrong.
And that’s equivalent to “don’t ask the AI, use a traditional resource,” right?
If you're a "regular person" vibe coder, you're not doing code reviews with a team anyway. You presumably had no teacher and no one to tell you your mistakes. So having a security bot is strictly an improvement.
If you're on a professional team, then you're presumably in the non-foolish group that already had standards, and is using it as a tool as with any of the other quality tools they use. And if they don't have standards and don't know this stuff already, well, the bot is again an improvement. It least it raises the issue for someone to ask what it means.
If you're a professional, I also assume you've heard of SQL injection (does it never come up in a CS degree?), so you don't really need more than a "this method is exposed to SQL injection" explanation. It's like saying "tail recursion is preferred because it compiles to a loop, so it's not prone to stack overflow". It assumes it doesn't need to elaborate further, but if you don't understand a term, you can just ask. Or look it up.
And yeah books or Wikipedia still exist even if you use an automated linter. You can go read about these things if you don't know them. I frequently tell my team members to go read about things. Actually I ended up in a digression about CSRF the other day (we work on low level networking, so generally not relevant), and I suggested the person I was talking to could go read about it if they're interested so as not to make them listen to me ramble.
Also I'm still unclear on why you think the explanation is crappy. It says the problem is you're making a query via simple string substitution, shows how you can abuse quotes if you do that (so concretely illustrates the problem), and says the reason the better solution is better is that it makes a structural object where you have a query with placeholders followed separately by parameters (so you can't misinterpret the query shape), which seems better than "strings are drawn from languages that give them their meanings"?
> Teams that decide to delegate security responsibilities to AI are more likely to do things fast and loose in general.
Note the word delegate. I claimed that teams that delegate security responsibilities to AI are more likely to play fast and loose in general. That’s because delegating security to AI—not supplementing existing security practices with AI—is likely to be a good way to launch insecure garbage into the world. AI simply isn’t good enough to get security right on its own. Maybe someday it will be good enough, but like I wrote earlier, it ain’t there yet. And any team that plays fast and loose with security is likely to play fast and loose in general.
See any problems with that logic?
I only used vibe coding as an obvious example that shows there are lots of teams that delegate security responsibilities to AI. (Vibe coders are delegating almost everything to AI.)
> If you're a "regular person" vibe coder, you're not doing code reviews with a team anyway. You presumably had no teacher and no one to tell you your mistakes. So having a security bot is strictly an improvement.
How is it strictly an improvement? Before vibe coding, “regular people” couldn't launch insecure garbage upon an unsuspecting world—they couldn't launch anything. Do you believe that it’s "strictly better" that now everyone can launch insecure garbage courtesy of their AI minions? Do you think it’s “strictly better” that lots of users are having their data sucked into insecure apps and web sites that are destined to be hacked?
> Also I'm still unclear on why you think the explanation is crappy.
It’s crappy because it tells you how to use a tool (a custom SQL interpolator) without helping you understand the cause of the problem that the tool is trying to solve. You could read this ChatGPT explanation about avoiding SQL injection in Scala and not be any wiser about how to avoid that problem in other programming languages.
Worse, you would never learn from this explanation that the underlying cause of SQL injection is the same as for cross-site-scripting holes and a host of other logic and security problems in software. That’s because ChatGTP was trained on explanations of these problems scraped from the internet, and 99% of those explanations are superficial because the people who wrote them didn’t understand the underlying issues.
But if you deeply understand the following, you will never make this kind of mistake again in any programming language:
1. Every string is drawn from an underlying language and must conform to the syntax and semantics of that language.
2. To combine strings safely, you must ensure that they are all drawn from the same language and are combined according to that language’s syntax and semantics.
Therefore, as a programmer, you must (a) understand the language beneath each and every string, (b) combine strings only when you can prove that they have the same underlying language, and (c) combine strings only according to that underlying language’s syntax and semantics.
If you understand these things, you will know how to avoid all SQL injection and XSS holes and related problems in all programming languages. Things like escaping will make sense: it converts a string in one language into its equivalent string in another language. Further, you will know when you can safely delegate some of your responsibilities to tools such as parsers, type systems, custom SQL interpolators, and the like.
But you wouldn’t learn any of this from the ChatGPT explanation you received. Worse, you wouldn’t even think to ask for this deeper explanation because you would have no reason to suspect from ChatGPT’s explanation that this deeper explanation even existed.
In any case, I appreciate your willingness to continue this conversation. It’s been fun and educational and has forced me to articulate some of my ideas more clearly. Thanks!
The actual problem is that you're using strings at all. The SQL solution (that the scala macros do) is to use prepared statements and bound parameters, not to escape the string substitution. Basically, work in the domain, not in the serialized representation (strings). Likewise with XSS, you put the stuff into a Text node or whatever and work directly with the DOM so the structural interpretation has all already happened before the user data is examined.
But "work in the domain as much as possible" is a good idea for a whole bunch of reasons (as chatgpt said).
It did also several times indicate there was more to the story. It didn't just say "because that way is safer"; it said it
> Builds a structured query object, not a raw string
> Parameterizes inputs safely (turns $id into ? + bound parameter)
> Often adds compile-time or runtime checks
> Instead of manipulating strings, you’re working with a query AST / fragment system
And concluded by saying there's absolutely more detail, and that it's important to understand:
> If you tell me which library you’re using (Doobie, Slick, Quill, etc.), I can show exactly what guarantees sql"..." gives in your stack—those details matter quite a bit.
On vibe coded "garbage", I expect there won't be much of a market for such things (why would there be when you can also just vibe it?), so it will more be a personal computing improvement, which already limits the blast radius (and maybe already improves the situation vs the precarious-by-default SaaS/cloud proliferation today even with poor security). I also think tooling and vibe security will be better than median professional level by the time it's actually as easy as people claim it is to vibe code an application anyway. i.e. security (which is an active area of improvement to sell to professionals) will probably be "solved" before ease-of-use. Partly exactly because issues like code injection are already "solved" in better programming languages (which are also more concise and have better tooling/libraries in general), so the bot just needs to default to those languages.
Do you believe that because you can delegate some responsibilities without sacrificing important requirements that it follows that you can delegate all responsibilities without sacrificing important requirements? Do you not understand the difference between delegating to the computer proofs such as type checking that the computer can discharge faithfully without error and delegating something as wide and perilous as security to something as currently flawed as AI?
> An LLM isn't an ideal solution to linting, but if you're stuck with a language with a weak type system maybe that's all you can reasonably do.
No, in such a situation you can add LLM-based checks to your responsibility for security. But you can’t delegate away your responsibility to LLMs and say that you care about security. AI ain’t there yet.
> The actual problem is that you're using strings at all.
What percentage of the world’s existing code do you believe does not use strings at all? Tragically, that is the world we live in.
> Basically, work in the domain, not in the serialized representation (strings).
Sure, but you can’t do all your work in the domain. At some point you must take data from the outside world as input or emit data as output. And, even if you are lucky enough to work in a domain where someone has done the parsing and serialization and modeling work for you so that you have the luxury of a semantic model to work with instead of strings, who had to write that domain library? What rules did that person have to know to write that library without introducing security holes?
> [ChatGPT] did also several times indicate there was more to the story.
Great. Then show me how a person who didn’t know of the existence of the rules I shared with you in my previous post would naturally arrive at them by continuing your conversation with ChatGPT.
> security (which is an active area of improvement to sell to professionals) will probably be "solved" before ease-of-use.
I think that this is a naive hope. Security is different from virtually all other responsibilities in computing, such as ease of use, because getting it right 99.99% of the time isn’t good enough. In security, there is no “happy path”: it takes just one vulnerability to thoroughly sink a system. Security is also different because you must expect that adversaries exist who will search unceasingly for vulnerabilities, and they will use increasingly novel and clever methods. Users won’t probe your system looking for ease-of-use failures in the UI. So if you think that AIs are going to get security right before ease-of-use, I think you are likely to be mistaken.
But it doesn't give you the same benefit. It gives you the partial benefit of catching these problems before they go to production, but it doesn't give you the remaining benefit of teaching your team about where their mental models are broken. A team that decides to delegate this responsibility entirely to AI is going to have a hard time learning about these serious defects in their mental models. Fixing those defects will pay dividends throughout the code base, not just in the places where AI would detect security failing.
'With enough eyes, all bugs are shallow' and AI is an automatable eye that looks at things we can tell nobody has seriously looked at before. It's not a panacea, there will be lots of false positives, but there's value there that we clearly aren't getting by 'just telling humans to use the tools available'.
See also: modern practices and sanitizers and tools and test frameworks to avoid writing memory errors in C, and the reality that we keep writing memory errors in C.
I think there's a difference in how trivial some of these things are to detect and how difficult others are. IDOR and SQLi aren't nearly as complex as C unsafety is.
If I were in charge of a 25 year old PHP application, tracking down every SQL query and converting it to a safe form would high on my list of priorities. You don't need AI for that, just ripgrep and a basic amount of care for your users.
Unfortunately you have no easy way of checking if closed source projects are similarly amateur.
Are you fuckin' serious? This would be caught with any self-respecting scanner even 5 years ago and with most educated juniors even earlier.
I use AI every day, but I'm not deep enough in the dilulu to believe that everything above two brain cells should be a transformer.
And these were also automatic. Looks very likely that the team didn’t give a damn about top basic security and good practices.
Like a house made of paper wouldn’t be an example of the insecurity of the construction industry.
agreed, though I think you'd be hard-pressed to find anyone who uses healthcare-related software professionally who thinks any "really strong development team" was involved in its creation.
People thinking that this isn't the case everywhere need a reality check. Most software is riddled with obvious security issues. If we can remediate them with AI, great, but don't be thinking that this is something that we could only have dealt with with AI. Enough attention and prioritization of these issues would also have sorted it.
Ask yourself if we weren't currently in an era of AI-focus and AI was just another boring tool, if we would be bothering to do this sort of thing. Loads of us still aren't bothering with basic static analysis.
Point is unless it eventually becomes cheap enough that we all have this at home and can run SOTA analysis ourselves, this too will pass. I imagine it will get cheap enough fwiw, but.. yea.
Not that I expect companies to be more proactive now. I have been disillusioned of that long ago. With AI they could be at least a little bit more proactive, which I guess is a great selling point of AI to corporate.
I'm sure that doesn't matter much to big tech folks seeking to fill that promotion packet though, or to executives seeking to demonstrate the overwhelming utility of this new income stream.
Back in 2010, as a security engineer, I also looked at OpenEMR. It was an absolute disaster, and was (and is) somewhat well-known as such. I found and published vulnerabilities very similar to these sixteen years ago. This is not exactly the Fort Knox of software.
It makes sense for AISLE to demonstrate that they're able to find vulnerabilities here, but I'd love to see a side-by-side comparison of modern SAST and DAST reviews. I bet we'd find similar vulnerabilities.
>I was the main contributor and maintainer to OpenEMR about ~20 years ago and then decided it was irredeemable and started over with ClearHealth/HealthCloud. Shockingly some of my code code lives on (from PHP 3). I am reluctant to say don't use it but if you do please don't expose it to anything public, which sadly happens most of the time. There are some real problems that exist in that code base from a security and HIPAA perspective.
Finding SQL injections etc is definitely valuable, but at the same time they did not hack Epic; the "100000 medical providers" number links to https://www.hhs.gov/sites/default/files/open-emr-sector-aler... which links open-emr.org/blog/openemr-is-proud-to-announce-seamless-support-for-telehealth/ which...404s. Per archive.org the source is something the CEO of now defunct lifemesh.ai said.
"medical record software" makes it sound super serious, but again OpenEMR should not be taken as seriously as for instance Epic.
Interesting... I have been working with many different EHR platforms across the country for the last 15 years and I have never heard of OpenEMR before, or any open-source platform for that matter.
I wrote an OSS PHP SAST tool 6 years ago, but it's suffered from industry neglect — most people only care about security after an incident, and PHP has enough magical behaviour that any tool needs to be tuned to how specific repositories behave.
I agree there's a big opportunity for LLMs to take this work forward, filling in for a lack of human expertise.
I stood up a Dokuwiki instance recently and had Qwen look through the codebase, and it didn't find anything critical. It identified "fragile patterns", though.
If you are sufficiently funded then you could benefit from the flip side of discovery but it looks bleak if you are a sole maintainer on a large project that is a dependency in many deployed instances without any revenue or donations, plus there is nobody digging deep enough to care or spend inference ( would your company spend the money on extra inference to is the question, more often than not) on both sides of the fence, we are going to see massive disruptions across the board.
Cybersecurity is becoming a proof-of-work of sorts and the race is on. There might be unknown number of zero days being silently discovered and deployed, likely have an impact on the economics too, thus making the access far more widespread.
I do wonder if this means our tech stacks will go back to being boring and simple as possible...you wouldn't hack a static html website being served on nginx would you?
Automation doesn't usually replace humans it just hikes up the floor.
I.e. nearly all of these (most in general?) bugs will be spotted quickly by a train eye. But it's hard to get trained eyes on code all the time. AI will catch all the low hanging fruit.
What's great about this it seems mostly low hanging I.e. even basic AI will help people patch holes.
I say this purely as a Software Engineer, not a security expert, but you have to consider hackers can, are, and will use AI against you.
The Mexican government was hacked by people using Claude[0] this was apparently many government systems and services, all that PII for everyone in the country in these systems. Even if Claude somehow "patches" this, there's so many open source models out there, and they get better every day. I've seen people fully reverse engineer programs from disassmebling their original code into compilable code in its original programmed language, Claude happily churning until it is fully translated, compiles and runs.
Whatever your thoughts on AI are, if you aren't at least considering it for security auditing (or to enhance security auditing) you are sleeping at the wheel just waiting to be hacked by some teenager skiddie with AI.
I've bounced back and forth on my feelings for AI and have landed in the realm of: - there are certain things it is exceptional at that humans cannot replicate. - there are certain things I do not want to use it for.
And review falls squarely in that first category. Similarly, it is exceptional at working through "low hanging fruit" type problems such as spotting inefficiencies, analyzing a profile to find flaws in software, etc.
Spotted over 100 “security issue but after whittling them down via reproduction scripts and validating they were real CVE’s - that number was around 30.
Even so - it was a huge win and something we wouldn’t have spotted.
It’s something I’ve now codified into repowarden.dev
Here, something that looks like the thing is a strong signal, as long as the probability is high enough to be useful.
Remember Netflix‘ chaos monkey?
OpenEMR may be in similar spaces.
Was this autonomous, as in "look at this repo and find me all the CVEs that could exist"?
Or was it much more guided?
if, during an automated code review, claude finds a vulnerability in a dependency, where should i direct it to share the findings?
who would be willing to take the slop-report, and validate it?
i've never done vulnerability disclosure, yet, with opus at max effort, i have found some security issues in popular frameworks/libraries i depend on.
a proper report can't be one pass, it has to validate it's a real problem, but ask opus to do that and you run the risk of the api refusing the request, endangering your account status. you ask to do it anyway, and write a report and now, you're burning tokens on a report that's likely to be ignore, because slop.
so i sit on this, and hope it doesn't hit me.
the skill would be manually triggered when vulnerabilities are found; do another pass for details; version, files, lines, then write a lightweight report and submit somewhere. anthropic could host this, or work with h1 to do that. when the models have extra capacity a process comes around and picks up these reports one by one, does another check, maybe with proof-of-concept, reports through proper channels.
it just doesn't sit well with me that, i am aware of something being broken, and not telling about it to someone who would otherwise want to know about it.
...so far !
===
Did they privately disclose these vulnerabilities to the developers and give them a reasonable amount of time to fix them, before they announced them to the world?
Because, and I'm going to highlight, if someone exploits a CVE in an EMR, they can wreck havoc on actual real patient data, and can endanger health and lives.
https://github.com/openemr/openemr/security
"Option 1 (preferred) : Report the vulnerability at this link. See Privately reporting a security vulnerability for instruction on doing this."
Did they do that?
Because if they didn't responsibly disclose, this sure seems like a hit job performed by someone who'd rather EMR software be closed source.
I'd love to see an opening paragraph like this one:
"All discovered vulnerabilities have already been patched. We waited to publish this article until they were. Release 8.0.3 addresses all of them, and we advise updating as soon as possible. We waited until 95% of installs had already updated to that version."