Securing AI-Generated Open Source Code

#1755187: Securing AI-Generated Open Source Code

Description:	Open source has always run on an unspoken assumption that somebody else is checking the code. AI-generated contributions are breaking that assumption faster than the ecosystem can adapt, turning implicit trust into a liability instead of a shortcut. Check out this post for the discussion that is the basis of our conversation on this week’s episode co-hosted by me, David Spark, the producer of CISO Series, and Steve Zalewski. Joining us is our sponsored guest, Abby Kearns, CEO, ActiveState. Trust, but nobody verifies The Heartbleed lesson keeps getting relearned. Robert Gezelter of Robert Gezelter Software Consultant pointed to that incident as the clearest case study of the problem, noting "the postmortem found that while multiple major firms shipped products using the software, all of them had economized by presuming that the peers were doing the work (and of course, paying the costs)." Checking machine-generated code is "tedious, time-consuming, and thus, expensive," he said, and the problem only surfaces once something breaks. Author Peter H. Gregory argued the cracks predate AI entirely. "Open source was crumbling prior to the widespread adoption of AI," he said. Since the movement's founding premise that "there would be numerous parties examining code for defects," it never matched reality, as "few actually do." The economics of trust just changed If code review ever could achieve scale with open source projects, that ship has sailed. "The bottleneck shifted from writing code to reviewing it, and review capacity never scaled with generation speed," said Jeremie Strand of SkillSafe.AI. He pointed to Curl dropping its bug bounty program as "the canary here." Joshua Copeland of Crescendo went further, arguing there's no putting this back in the bottle. "We are not going to stop developers from using AI code generators, and pretending we can ban the behavior will only push it into the shadows," he said. What changed, in his view, is that "AI has collapsed the cost of producing code, pull requests, vulnerability reports, and convincing technical noise," breaking the assumption that "contribution required effort and that human attention could roughly keep pace." His prescription is not resistance but redesign, to "rewrite the rules around provenance, disclosure, review, signed releases, dependency isolation, maintainer funding, and organizational accountability." Implicit trust doesn't scale anymore If the code compiles, that used to be enough. Not anymore. Tim Shelton of HAWK Network Defense framed the shift as one of trust rather than value. "It lowers the cost of producing code, dependencies, pull requests, vulnerability reports, and technical noise," he said, creating pressure on both sides of the ecosystem. "That does not mean open source is broken. It means implicit trust does not scale anymore." The fix, in his view, runs through "provenance, signed releases, dependency isolation, build attestations, SBOM accuracy, maintainer funding, and organizational accountability for what gets pulled into production." Adam Palmer, CISO at First Hawaiian Bank, distilled the same point into fewer words: "I don't think AI is killing open source, but it's exposing the risks that were already there." Verification, he said, "must now become continuous... The future isn't necessarily less open source. It's more provenance and far less implicit trust." Rethinking what "open" even means Some practitioners are questioning whether the old open-source calculus still holds. Tyson Kopczynski of Whiterabbit was blunt about it, saying "the benefits around open source are melting away," to the point that "it again makes more sense to just roll 'your own source.'" Aaron Stanley, former VP of security for dbt Labs, saw more upside in the disruption. "Will AI change the risk profile of OSS? Absolutely, but maybe it will be for the better," he said. As the cost of writing code drops, he argued, teams can use AI to "write functions that are based on OSS but are unique to your use case," pulling only what's needed from a project "and possibly excluding the part of the code that is insecure." But what does the ecosystem look like "when everyone is a dependent on part of a project?" His bet is that done well, it produces "a sort of anti-commons where everyone invests in maintaining their smaller fork of the project and commits back improvements that can be rationalized against the original source."
More info:	https://www.linkedin.com/pulse/securing-ai-generated-open-source-code-cisoseries-37yjc/?trackingId=PBJHFfqGTbm8OUwv9DMBsA%3D%3D

Description:

Open source has always run on an unspoken assumption that somebody else is checking the code. AI-generated contributions are breaking that assumption faster than the ecosystem can adapt, turning implicit trust into a liability instead of a shortcut.

Check out this post for the discussion that is the basis of our conversation on this week’s episode co-hosted by me, David Spark, the producer of CISO Series, and Steve Zalewski. Joining us is our sponsored guest, Abby Kearns, CEO, ActiveState.

Trust, but nobody verifies
The Heartbleed lesson keeps getting relearned. Robert Gezelter of Robert Gezelter Software Consultant pointed to that incident as the clearest case study of the problem, noting "the postmortem found that while multiple major firms shipped products using the software, all of them had economized by presuming that the peers were doing the work (and of course, paying the costs)." Checking machine-generated code is "tedious, time-consuming, and thus, expensive," he said, and the problem only surfaces once something breaks. Author Peter H. Gregory argued the cracks predate AI entirely. "Open source was crumbling prior to the widespread adoption of AI," he said. Since the movement's founding premise that "there would be numerous parties examining code for defects," it never matched reality, as "few actually do."

The economics of trust just changed
If code review ever could achieve scale with open source projects, that ship has sailed. "The bottleneck shifted from writing code to reviewing it, and review capacity never scaled with generation speed," said Jeremie Strand of SkillSafe.AI. He pointed to Curl dropping its bug bounty program as "the canary here." Joshua Copeland of Crescendo went further, arguing there's no putting this back in the bottle. "We are not going to stop developers from using AI code generators, and pretending we can ban the behavior will only push it into the shadows," he said. What changed, in his view, is that "AI has collapsed the cost of producing code, pull requests, vulnerability reports, and convincing technical noise," breaking the assumption that "contribution required effort and that human attention could roughly keep pace." His prescription is not resistance but redesign, to "rewrite the rules around provenance, disclosure, review, signed releases, dependency isolation, maintainer funding, and organizational accountability."

Implicit trust doesn't scale anymore
If the code compiles, that used to be enough. Not anymore. Tim Shelton of HAWK Network Defense framed the shift as one of trust rather than value. "It lowers the cost of producing code, dependencies, pull requests, vulnerability reports, and technical noise," he said, creating pressure on both sides of the ecosystem. "That does not mean open source is broken. It means implicit trust does not scale anymore." The fix, in his view, runs through "provenance, signed releases, dependency isolation, build attestations, SBOM accuracy, maintainer funding, and organizational accountability for what gets pulled into production." Adam Palmer, CISO at First Hawaiian Bank, distilled the same point into fewer words: "I don't think AI is killing open source, but it's exposing the risks that were already there." Verification, he said, "must now become continuous... The future isn't necessarily less open source. It's more provenance and far less implicit trust."

Rethinking what "open" even means
Some practitioners are questioning whether the old open-source calculus still holds. Tyson Kopczynski of Whiterabbit was blunt about it, saying "the benefits around open source are melting away," to the point that "it again makes more sense to just roll 'your own source.'" Aaron Stanley, former VP of security for dbt Labs, saw more upside in the disruption. "Will AI change the risk profile of OSS? Absolutely, but maybe it will be for the better," he said. As the cost of writing code drops, he argued, teams can use AI to "write functions that are based on OSS but are unique to your use case," pulling only what's needed from a project "and possibly excluding the part of the code that is insecure." But what does the ecosystem look like "when everyone is a dependent on part of a project?" His bet is that done well, it produces "a sort of anti-commons where everyone invests in maintaining their smaller fork of the project and commits back improvements that can be rationalized against the original source."

More info:

https://www.linkedin.com/pulse/securing-ai-generated-open-source-code-cisoseries-37yjc/?trackingId=PBJHFfqGTbm8OUwv9DMBsA%3D%3D

Date added	Sept. 24, 2026, 10:24 p.m.
Source	Linkedin
Subjects	AI/ML - Artificial Intelligence / Machine Learning / GenAI / Artificial General Intelligence - AGI - Various PodCasts / Webcast / Webinar / eSummit / Virtual Event etc. Security Management/Strategic Security/ROI/ROSI - CISO and Higher Level