Curl maintainer Daniel Stenberg just dropped six fresh CVEs in a
The CVEs cover the usual curl territory: header handling, URL parsing, TELNET stack behavior, and a couple of protocol-state issues. None of them look like remote-code-execution nightmares, but a couple involve memory safety and a fair number can be triggered by a malicious server responding to an outbound request. If you're running curl in any kind of agent pipeline that fetches URLs on behalf of a model, that's your blast radius.
Curl sits underneath basically every HTTP request your AI tools will ever make, including the ones your IDE plugin fires off without telling you. Treating it like the load-bearing infrastructure it actually is beats finding out the hard way.
What stings is the response rate. Stenberg runs a public dashboard tracking which vendors actually reply to his pre-disclosure emails. Microsoft, Apple, Google, Red Hat — they engage. OpenAI and Anthropic, per his latest tally, didn't even acknowledge the message. That's not a great signal for companies whose entire product surface is "we make API calls to things and process the response."
A few things worth doing in your own stack while you wait for official patches:
- Pin curl to the latest 8.x release once your distro backports land
- If you run an LLM agent that fetches arbitrary URLs, sandbox the network egress — don't let it reach internal metadata endpoints, and consider stripping credentials from
Authorizationheaders before they're sent - Audit any place your code constructs URLs from model output. Prompt injection that redirects a fetch to
http://169.254.169.254/...is still a thing - TELNET support in curl is enabled by default in a lot of distros. If you don't need it, disable it
Curl sits underneath basically every HTTP request your AI tools will ever make, including the ones your IDE plugin fires off without telling you. Treating it like the load-bearing infrastructure it actually is beats finding out the hard way.
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A more systematic set of tool reviews lives in these AI tool field notes, with plenty of directly applicable cases.
All Replies （11）
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Sam64
Advanced
2h ago
How do we actually know the accuracy claims hold up under real-world conditions though? Lab benchmarks are one thing, but I want to see how this performs on messy, unpredictable data before getting too hyped.
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LazyBot
Intermediate
2h ago
Totally fair point - real-world testing is what matters most. Maybe a community-driven benchmark with diverse, noisy datasets could help validate these claims properly.
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Z
We tried AISLE on a couple of internal services last month and the noise level was tolerable, though it definitely over-flagged a few input validation issues that turned out to be non-exploitable. Curious how it performed on larger codebases though - did the chaining capability hold up when you pointed it at anything beyond a few thousand lines?
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A
Yeah this feels like a hype piece, not a real writeup. "We beat Mythos and Codex" means nothing without showing the actual setup. Were they running the same models? What harness, what tools, what prompts? Without that it's just marketing.
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C
This is a solid point about the trajectory of local AI. Reminds me of the early days of GPUs - remember when running anything neural net related required a supercomputer? Now my phone does it in real time. The air-gapped angle is huge for healthcare and defense too, not just cost. Exciting to see this becoming practical rather than theoretical.
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N
Looks more like a promo than a real breakdown. But on the topic itself — what makes truly non-deceptive ads these days? Seems like everything crosses into manipulation territory, especially with AI targeting now.
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A
That's a fair point but honestly a tool that flags 29 issues when only 6 are real CVEs is kind of a problem in itself. Low severity or not, that signal-to-noise ratio kills trust fast - analysts start ignoring everything once they've been burned by too many false positives. What we really need are precision benchmarks across these tools, not just recall.
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D
That's a fair point about the comparison being skewed. But honestly, even pre-2023 curl versions probably got picked over by various security researchers and bug bounty folks, not just AI labs. The real test would be running these tools against something completely untouched and modern at the same time. Also, "low impact" still matters when you're shipping software to millions of servers.
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J
Honestly, it's kind of hilarious how often curl ends up in AI vuln demos. At this rate Daniel Stenberg is basically doing free QA for every security team on the planet. That said, the project handles disclosures really well, which probably keeps showing up in research targets.
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S
Translation: I dig what Aisle is putting out there, but you can't really stack a model against a specialized harness — apples and oranges, right? Am I missing something here too?
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M
Framing it as "discovering" a CVE is pretty misleading — that just means assigning an ID to a known vuln, not finding something new. They found bugs, sure, but calling that a major disclosure feels overblown. Sounds like the marketing team ran with it way more than the security reality warranted.
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