Only 6.8% of respondents to a security survey resolve issues in AI-generated code inside the coding assistant, at the moment the code is written.

That figure comes from Salt Security's State of Agentic AI and API Security 2H 2026, published on 13 October. It's based on more than 380 responses from security, technology, architecture, application security, API platform and DevOps professionals, about a third of them in technology companies. Salt says the rest of the issues "land in code review, testing, or after deployment". In the same survey, 83.5% of respondents estimated that AI-generated or AI-assisted code contributes to at least some of their application security findings.

The 6.8% describes where issues get fixed, not how many there are. It doesn't say whether the rest are caught a minute later in review or a month later in production, and Salt's press release doesn't split them. It's self-report from practitioners describing their own organisation's process, not an audit of anyone's pipeline, and the release gives no dates for when the survey ran. Salt also sells API and agent security products, so it has an interest in the problem looking large.

It measures process. A team could be in the other 93% because its assistant never flags anything, or because it has chosen to keep security checks in CI where they already run.

On 10 October, fifteen researchers posted the Last Secure Code Benchmark to arXiv. They built 450 coding tasks from 150 real vulnerabilities across seven languages and gave them to six frontier coding agents, five running in Claude Code and one in Codex. The task descriptions asked only for working functionality and never mentioned security. Hidden tests then checked both. The agents produced working code on between 73.6% and 91.3% of tasks, but the best of them, Claude Fable 5, produced code that was both working and secure only 25.1% of the time.

The researchers then reran four of the models with one sentence added to each task, asking for an implementation that was "secure and free of any plausible vulnerabilities". Three of the four improved by between 15.6 and 21.5 points. The fourth barely moved, and the paper notes that "most tasks stay insecure even with the reminder."

On 15 October, GitHub made AI Scan generally available. It looks for vulnerabilities in pull requests written in languages its CodeQL scanner doesn't cover, such as PHP, shell scripts, Terraform and Dockerfiles. Its findings are advisory. They "cannot block pull request merges", and it doesn't run on pull requests from forks.

A day later InfoQ reported Chainguard's two new beta products. Workspaces runs each developer's and agent's work in a hardened, isolated remote environment. Checks "moves continuous integration into the development loop", so an agent can call the tests itself and get the failure logs back mid-session to try again.

On the benchmark's evidence, an agent that isn't asked for secure code mostly doesn't write it.

The benchmark goes some way to explaining Salt's number. On the benchmark's evidence, an agent that isn't asked for secure code mostly doesn't write it. If everyday requests look like the benchmark's, describing a feature and saying nothing about security, then generation is the step where security is least likely to come up at all. The reminder results point the same way. For three of the four models, one sentence of instruction made a bigger difference than the gap between the best and worst agents in the main run.

The tooling shipped in October mostly sits where the other 93% already work. GitHub's scanner reads the pull request after the agent has finished, and can only advise. Chainguard's Checks is the nearer attempt to put security inside the session, because the agent runs the tests itself before anything is pushed. Both are product launches, one of them a 30-day beta trial, with no published results on what they catch.

What the pairing can't show is whether fixing earlier would cut the findings Salt's respondents see. The benchmark's tasks were built from known vulnerabilities, not from those respondents' code, and nothing here measures how many issues the 6.8% let through compared with everyone else.

Check whether the standing instructions your coding agents work from mention security at all. The benchmark's reminder was a single sentence. Then take last month's security findings on AI-assisted code and note the stage each one was caught at.

Sources

  1. Salt Security, "State of Agentic AI and API Security 2H 2026", 13 October 2026 — https://salt.security/press-releases/state-of-agentic-ai-and-api-security-2h-2026
  2. Wu, T., Ge, C., Chen, P., Zhang, F. et al., "Last Secure Code Benchmark: Is the Code You Vibe Secure?", 10 October 2026 — https://arxiv.org/abs/2610.14766
  3. GitHub, "AI Scan for pull requests generally available", 15 October 2026 — https://github.blog/changelog/2026-10-15-ai-scan-for-pull-requests-generally-available/
  4. InfoQ, "Chainguard Launches Secure Workspaces and Checks for AI Coding Agents", Matt Saunders, 16 October 2026 — https://infoq.com/news/2026/10/chainguard-workspaces-checks