Some insider knowledge: Lilli was, at least a year ago, internal only. VPN access, SSO, all the bells and whistles, required. Not sure when that changed.
McKinsey requires hiring an external pen-testing company to launch even to a small group of coworkers.
I can forgive this kind of mistake on the part of the Lilli devs. A lot of things have to fail for an "agentic" security company to even find a public endpoint, much less start exploiting it.
That being said, the mistakes in here are brutal. Seems like close to 0 authz. Based on very outdated knowledge, my guess is a Sr. Partner pulled some strings to get Lilli to be publicly available. By that time, much/most/all of the original Lilli team had "rolled off" (gone to client projects) as McKinsey HEAVILY punishes working on internal projects.
So Lilli likely was staffed by people who couldn't get staffed elsewhere, didn't know the code, and didn't care. Internal work, for better or worse, is basically a half day.
This is a failure of McKinsey's culture around technology.
McKinsey has a weird structure where there are too many cooks in the kitchen.
Everybody there is reviewed on client impact, meaning it ends up being an everybody-for-themselves situation.
So as a developer you have little guidance (in fact, you're still being reviewed on client impact, even if you have 0 client exposure).
Then a (Senior) Partner comes in with this idea (that will get them a good review), and you jump on that. After all, it's all you can do to get a good review.
You work on it, and then the (Senior) Partner moves on. But it's not done. It's enough for the review, but continuing to work on it doesn't bring you anything, in fact, it will actually pull you down, as finishing the project doesn't give immediate client results.
So what does this mean? Most products of McKinsey are a grab-bag of raw ideas of leadership, implemented as a one-off, without a cohesive vision or even a long-term vision at all. It's all about the review cycle.
McKinsey is trying to do software like they do their other engagements. It doesn't work. You can't just do something for 6 months and then let it go. Software rots.
The fact that they laid off a good amount of (very good) software engineers in 2024 is a reflection on how they see software development.
And McKinsey's people, who go to other companies, take those ideas with them. Result: The UI of your project changes all the time, because everybody is looking at the short-term impact they have that gets them a good review, not what is best for the project in the long term.
According to levels the pay band caps out around $250k and a principal title. It's good but probably not enough for most to put up with the culture long term.
As an ex-consultant: consulting at that level is kind of a grift. They over-promise and under-deliver as SOP. It's ripe for AI disruption, whatever that looks like.
Ideally, executives will get replaced by AI soon. Which should actually be easier than engineers. That will kind of solve the consulting problem automatically.
It’s really about bypassing the existing power structure of the company. Competence of the work itself is a secondary objective. Most in-house initiatives can be slow rolled by management.
The fresh faced consultant with 2-3 steps to access the CEO neutralizes that. It seems grifty but is really exploiting bugs in corporate governance.
The current fad of firing the managers is a riff on this. Every jackass C-level is coming up with the novel idea of flattening.
Fair take, but you'd be hard pressed to find much resemblance to any advice McK gives to its own practices.
Pre-AI, I always said McK is good at analysis, if you need complicated analysis done, hire a consulting firm.
If you need strategy, custom software, org design, etc. I think you should figure out the analysis that needs to be done, shoot that off to a consulting firm, and then make your decision.
IME, F500 execs are delegation machines. When they wake up every morning with 30 things to delegate, and 25 execs to delegate to, they hire 5 consulting teams. Whether you hire Mck, or Deloitte, or Accenture will only come down to:
1. Your personal relationships
2. Your company's policies on procurement
3. Your budget
in that order.
McK's "secret sauce" is that if you, the exec, don't like the powerpoint pages Mck put in front of you, 3 try-hard, insecure, ivy-league educated analysts will work 80 hours to make pages you do like. A sr. partner will take you to dinner. You'll get invited to conferences and summits and roundtables, and then next time you look for a job, it will be easier.
Analysis of what? What does that mean? What's something you conceivably would need a consulting firm to "analyze?" I don't understand why management consulting firms would hire software people in the first place, and then punish them for not being on a client-facing project. That seems a bit contradictory to me, but this is all way out of my wheelhouse
2. How is the industrial ceramic market structured, how do they perform
3. How does a changing environment impact life insurance
Strategy:
1. Should I build a datacenter
2. Should I invest in an industrial ceramics company
3. Should I divest my life insurance subsidiary
Specifically in the software world this would be "automate some esoteric ERP migration" or "build this data pipeline" vs. "how can we be more digital native" or "how do we integrate more AI into our company"
The executives who hire McKinsey are often not clueless, but they often lack the political power in the company to push through their plans. So they hire some well-regarded business consultancy to get an "objective" analysis what needs to be done.
The purpose of hiring them is to make them come to the conclusion you already have, so when it goes well you get the credit for doing it, or if it goes sideways you can pin the blame on them.
Most companies are not _just_ tech companies and don't have business analysts, consulting analysts, solutions consultants, software engineers and DBA's on staff.
Many, many, many companies are very happy with the consulting firms they hire.
Of course, those are the consulting firms that aren't publicly traded and in the news all the time (for all the wrong reasons).
> One of those unprotected endpoints wrote user search queries to the database. The values were safely parameterised, but the JSON keys — the field names — were concatenated directly into SQL.
I was expecting prompt injection, but in this case it was just good ol' fashioned SQL injection, possible only due to the naivety of the LLM which wrote McKinsey's AI platform.
I just wonder how much professional grade code written by LLMs, "reviewed" by devs, and commited that made similar or worse mistakes. A funny consequence of the AI boom, especially in coding, is the eventual rise in need for security researchers.
I guess you could argue that github wasn't vulnerable in this case, but rather the author of the action, but it seems like it at least rhymes with what you're looking for.
The tacit knowledge to put oauth2-proxy in front of anything deployed on the Internet will nonetheless earn me $0 this year, while Anthropic will make billions.
I don’t love the title here. Maybe this is a “me” problem, but when I see “AI agent does X,” the idea that it might be one of those molt-y agents with obfuscated ownership pops into my head.
In this case, a group of pentesters used an AI agent to select McKinsey and then used the AI agent to do the pentesting.
While it is conventional to attribute actions to inanimate objects (car hits pedestrians), IMO we should be more explicit these days, now that unfortunately some folks attribute agency to these agentic systems.
> This was McKinsey & Company — a firm with world-class technology teams [...]
Not exactly the word on the street in my experience. Is McKinsey more respected for software than I thought? Otherwise I'm curious why TFA didn't just politely leave this bit out.
Can we stop softening the blow? This isn't "drafted with at least major AI help", it's just straight up AI slop writing. Let's call a spade a spade. I have yet to meet anyone claiming they "write with AI help but thoughts are my own" that had anything interesting to say. I don't particularly agree with a lot of Simon Willison's posts but his proofreading prompt should pretty much be the line on what constitutes acceptable AI use for writing.
Grammar check, typo check, calls you out on factual mistakes and missing links and that's it. I've used this prompt once or twice for my own blog posts and it does just what you expect. You just don't end up with writing like this post by having AI "assistance" - you end up with this type of post by asking Claude, probably the same Claude that found the vulnerability to begin with, to make the whole ass blog post. No human thought went into this. If it did, I strongly urge the authors to change their writing style asap.
"So we decided to point our autonomous offensive agent at it. No credentials. No insider knowledge. And no human-in-the-loop. Just a domain name and a dream."
Your reaction is worse than the article. There's no way you could know for sure what their writing process was, but that doesn't stop you from making overconfident claims.
One thing I've learned recently is a lot guys (like here) have been out here reading each word of a given company's tech blog, closely parsing each sentence construction.. I really cant imagine being even concious of the prose for something like this. A corporate blog, to me, has some base level of banality to it. It's like reading a cereal box and getting angry at the lack of nuance.
Like who cares? Is there really some nostalgia for a time before this? When reading some press release from a cybersecurity company was akin to Joyce or Nabakov or whatever? (Maybe Hemingway...)
We really gotta be picking our battles here imo, and this doesn't feel like a high priority target. Let companies be the weird inhuman things that they are.
Read a novel! They are great, I promise. Then when you read other stuff, maybe you won't feel so angry?
That's the problem with AI writing in a nutshell. In a blind, relatively short comparison (similarly used for RLHF), AI writing has a florid, punchy quality that intuitively feels like high quality writing.
But then after you read the exact same structure a dozen times a day on the web, it becomes like nails on the chalkboard. It's a combination of "too much of a good thing" with little variation throughout a long piece of prose, and basic pattern recognition of AI output from a model coalescing to a consistent style that can be spotted as if 1-3 human ghost writers wrote 1/4 of the content on the web.
A vibe? It’s completely obvious AI slop with no attempt to make it legible. They didn’t even prompt out the emdashes. For such a cool finding this is extremely disappointing.
My take*: McKinsey hiring largely selects for staying calm under pressure and presenting a confident demeanor to clients. Verbal fluency with decision-making frameworks goes a long way. Having strong analytical skills seemed essential; hopefully the bar for "sufficiently analytical" has raised along with general data science skills in industry.
I don't view them as top-tier experts in their own right, whether it be statistics or technology, but they have a knack for corporate maneuvering. I often question their overall value beyond the usual "hire the big guns to legitimize a change" mentality. Maybe a useful tradeoff? I'd rather see herd-like adoption of current trends than widespread corporate ignorance and insularity.**
A huge selling point for M&Co is kind of a self-fulfulling prophecy based on the access they get. This gives them a positive feedback loop to find the juiciest and most profitable areas to focus on.
For those who know more, how do my takes compare?
* I interviewed with them over 15 years ago, know people who have worked there, and I pay attention to their reports from time to time.
** Of course, I'd rather see a third way: cross-pollination between organizations to build strong internal expertise and use model-based decision making for nuanced long-term decisions... but that's just crazy talk.
> Having strong analytical skills seemed essential
and
> they have a knack for corporate maneuvering
One way to view this is that the above combination of skills is both rare and very useful. That means it's expensive. So instead of hiring someone like that at "full rate" and keeping them around, you can "borrow" them from McK to solve a problem your regular crew can't (or isn't able to) for various reasons.
Plus, as one manager of mine said many years ago:
"We use consultants b/c they are both easy to hire AND easy to fire"
No, they don't have world class technology teams, they hire contractors to do all the tech stuff, their expertise is in management, yes that's world class.
Is it though? Managing teams to not torpedo your company with stupid stuff like this is kinda core to “good management.” The evidence would indicate they’re not very good at that either.
It’s a self fulfilling prophecy. They’re extremely expensive so they must be good so they must be worth it. And because at that level measurement is extremely subjective it’s mainly about the vibes.
> Not exactly the word on the street in my experience.
Depends on the street you're on. Are you on Main Street or Wall Street?
If you're hiring them to help with software for solving a business problem that will help you deliver value to your customers, they're probably just like anyone else.
If you're hiring them to help with software for figuring out how to break down your company for scrap, or which South African officials to bribe, well, that's a different matter.
One interesting takeaway here is how quickly AI agents expose weaknesses in internal systems.
Many enterprise tools were designed assuming human interaction, where authentication flows, manual reviews, and internal processes add implicit safeguards.
But once you introduce autonomous agents that can systematically probe endpoints, missing authorization checks or misconfigured APIs become much easier to discover and exploit.
I suspect we’ll see a growing need for automated validation layers that continuously test internal AI tools for access control, data exposure, and unintended behaviors before they’re widely deployed.
I've got no idea who codewall is. Is there acknowledgment from McKinsey that they actually patched the issue referenced? I don't see any reference to "codewall ai" in any news article before yesterday and there's no names on the site.
- "The agent mapped the attack surface and found the API documentation publicly exposed — over 200 endpoints, fully documented. Most required authentication. Twenty-two didn't."
What I don't see in this article that should be explicit:
If your data is in this database, it's gone. Other people have it. Your sensitive data that you handed over to their teams has vanished in a puff of smoke. You should probably ask if your data was part of the leak.
Fail to see how a state actor would not have come across this already.
> named after the first professional woman hired by the firm in 1945
Going out of their way to find a woman's name for an AI assistant and bragging about it is not as empowering as the creators probably thought in their heads.
Could the author please provide the prompt that was used to vibe write this blog post? The topic is interesting, but I would rather read the original prompt, as I am not sure which parts still match what the author wanted to say, vs flowerly formulations for captivating reading that the LLM produced.
One interesting takeaway here is how quickly organizations are deploying AI tools internally without fully adapting their security models.
Traditional application security assumes fairly predictable inputs and workflows, but LLM-based systems introduce entirely new attack surfaces—prompt injection, data leakage, tool misuse, etc.
It feels like many enterprises are still treating these systems as just another SaaS product rather than something closer to an autonomous system that needs a different threat model...
I think the underlying point is valid. Agents are a potential tool to add to your arsenal in addition to "throw shit at the wall and see what sticks" tools like WebInspect, Appscan, Qualys, and Acunetix.
Founder of CodeWall here. It's quite funny because whilst an LLM did write the bulk of the posts factual content (based on the agents findings), I wrote the intro and summary at the end. That's just my writing style. Feel free to read my personal blog to compare: https://darkport.co.uk
Idk how big your team is of course but imo try to hire a technical writer (they’re really cheap now), it pays dividends for a long time as consistent style and keywords build up SEO reputation. This article is making the rounds, some bigger papers picked it up, it is very valuable to land it well.
If you really DID come up with that paragraph 100% completely on your own with no LLM influence then...I apologize for the insult, though I can't really back out from what I said. It's still a bombastic way of saying very little.
It's an actual story telling method, molded into a supposed to be informative article with a bunch of "please make it interesting" sprinkled on top of it. These day known as the what's left of the internet.
I wonder how these offensive AI agents are being built? I am guessing with off the shelf open LLMs, finetuned to remove safety training, with the agentic loop thrown in.
Honestly you can point regular Claude Code or Codex CLI at a web app and tell it to start a penetration test and get surprisingly good results from their default configurations.
parameterized values but raw key concatenation is the kind of thing that looks safe in code review. easy to miss for humans, but an agent will just keep poking at every input until something breaks.
With all we've been learning from stuff like the Epstein emails, it would have been nice if someone had leaked this data:
> 46.5 million chat messages. From a workforce that uses this tool to discuss strategy, client engagements, financials, M&A activity, and internal research. Every conversation, stored in plaintext, accessible without authentication.
> 728,000 files. 192,000 PDFs. 93,000 Excel spreadsheets. 93,000 PowerPoint decks. 58,000 Word documents. The filenames alone were sensitive and a direct download URL for anyone who knew where to look.
I'm sure lots of very informative journalism could have been done about how corporate power actually works behind the scenes.
That information is likely already in the hands of various folks as I highly doubt the authors were the first to find this glaring security issue, they’re likely only the first to disclose it. If McKinsey has hard data that nobody else exploited this now would be a good time to disclose that given what sounds like an extremely severe data leak.
The chat messages are very very sensitive. You could easily reverse engineer nearly every ongoing Mck engagement. The underlying data is not as sensitive, its decades of post-mortems, highly sanitized. No client names, no real numbers.
> No credentials. No insider knowledge. And no human-in-the-loop. Just a domain name and a dream. ... Within 2 hours, the agent had full read and write access to the entire production database.
Having seen firsthand how insecure some enterprise systems are, I'm not exactly surprised. Decision makers at the top are focused first and foremost on corporate and personal exposure to liability, also known as CYA in corporate-speak. The nitty-gritty details of security are always left to people far down the corporate chain who are supposed to know what they're doing.
Not exactly clear from the link: were they doing red team work for McKinsey or is this just "we found a company we thought wouldn't get us arrested and ran an AI vuln detector over their stuff"?
You'd think that the world's "most prestigious consulting firm" would have already had someone doing this sort of work for them.
From TFA: "Fun fact: As part of our research preview, the CodeWall research agent autonomously suggested McKinsey as a target citing their public responsible diclosure policy (to keep within guardrails) and recent updates to their Lilli platform. In the AI era, the threat landscape is shifting drastically — AI agents autonomously selecting and attacking targets will become the new normal."