Raoof Altaher
In this section
On this page

Using AI Agents to Keep Pace with Vulnerability Response

6 min read

The past few days have been a reminder of how much modern software depends on components we rarely think about until something goes wrong. Critical vulnerabilities in PHP and Node.js, alongside a compromised LiteLLM release, have highlighted different risks across the software stack.

A grey-violet title card reading Using AI Agents to Keep Pace with Vulnerability Response, beside blurred pink and orange shapes

These incidents raise an important question: how can developers, infrastructure teams, and security professionals keep pace with vulnerabilities that affect widely used components and the applications built on top of them?

AI agents can help investigate vulnerabilities, identify exposed dependencies, review code, and accelerate remediation. But AI agents do not replace cybersecurity expertise. They become far more useful when guided by experienced security engineers, DevOps and DevSecOps professionals, and developers who understand the systems they are protecting. An expert can set the scope, provide the right context, challenge weak findings, and validate the proposed fix. The goal is not to replace professional judgment, but to help experts apply their knowledge across more projects, in less time, and with greater consistency.

Three different failures, one urgent lesson

PHP2Shell: On October 21, PHP released fixes for two critical flaws, CVE-2026-109618 and CVE-2026-109619. Under affected configurations, a remote attacker could execute code without authenticating first. Because the vulnerabilities affect PHP itself, applications may inherit the risk through their runtime. Review the PHP advisories and vendor guidance and upgrade to a fixed version supported for your deployment.

Node.js security release: The October 21 Node.js security release addressed ten issues. One headline issue, CVE-2026-111497, affects the bundled Brotli decoder and can permit remote code execution when an application decompresses attacker-controlled data. Imperva demonstrated code execution against a vulnerable official Node.js build. The same release addressed a separate HTTP keep-alive response-handling issue, along with DNS and TLS vulnerabilities. Check the advisory and update to a fixed version for your supported Node.js line.

LiteLLM supply-chain compromise: This was not a conventional CVE. On October 21, a malicious litellm==1.107.0 package and affected container tags were published through a compromised release process. The package contained malware designed to steal credentials and enable further compromise. The maintainer’s incident report describes the attack chain. If an affected artifact ran in your environment, follow the maintainer’s incident guidance. Simply upgrading may not be enough if credentials were exposed.

The incidents are different, but the response pattern is familiar: establish whether you are affected, gather evidence, prioritize the risk, apply a verified fix, and check whether exposure already occurred.

AI is becoming part of vulnerability research

There is a concrete AI connection here, but it is important to describe it accurately. In its PHP2Shell research, Wiz describes how frontier AI models helped uncover the flaws and made complex exploit development more practical. The Node.js advisory credits OpenAI Codex Security researchers Khai Tran and Sylvie Mayer with reporting the Brotli vulnerability.

This is evidence that AI can contribute to serious security research. It is not evidence that every incident this week was discovered by AI or that all of them involved autonomous agents. The broader lesson is that AI can shorten parts of the discovery, investigation, and remediation cycle. Attackers can benefit from that speed, and defenders need to use it responsibly too.

Manually checking every dependency, code path, secret, build workflow, and agent integration across several projects is difficult to scale. A security review that happens only after a headline, or only when a person has spare time, risks leaving gaps.

How I use Ultrapowers Vulnerability Hunter

I built Vulnerability Hunter in Ultrapowers to make security review a repeatable part of development, rather than an occasional checklist. As an AI engineer and cybersecurity advisor, I use it as one part of my professional workflow. I recently used it across multiple personal and client projects to investigate Node.js exposure and move quickly toward focused remediation.

The workflow is designed to help an experienced practitioner investigate consistently:

  1. Establish the scope. Review project context, manifests, available audit tools, and the areas that need investigation.
  2. Investigate in parallel. Focused checks examine dependency advisories, insecure code patterns, secrets and supply-chain risks, AI and agent surfaces, and relevant public exposure.
  3. Challenge important findings. High- and medium-severity findings are sent to a verifier that looks for evidence that could refute them. The aim is to reduce false positives, not simply produce a long list of warnings.
  4. Deliver an evidence-backed report. Findings, supporting file paths, skipped checks, and an overall verdict give the engineer concrete information to assess and act on.

One important boundary: Vulnerability Hunter is read-only. It does not silently edit source code, install packages, or deploy changes. It reports evidence and remediation guidance; I review the findings, decide what to fix, and verify changes through the project’s normal testing and release process. The skill’s documented contract also limits what it treats as a finding and requires failed or skipped checks to be reported rather than disguised as successful coverage.

From one investigation to multiple projects

The advantage is not only speed on a single repository. With an appropriate agent setup, I can run scoped investigations across multiple repositories and projects in parallel, including work for different clients. That makes it practical to begin reviewing many projects at once instead of handling each one only after the previous review is finished.

Parallel execution does not make the results automatically correct or complete. Project scope, access, available audit tools, and verification still matter. My expertise and the team’s methodology guide the investigation, and people remain responsible for prioritizing findings, approving changes, and validating the outcome. The agent helps extend that expertise across more work; it does not replace it.

What teams should do now

For teams responding to these disclosures, the immediate steps are practical:

  • Patch affected PHP and Node.js runtimes using the relevant upstream advisory.
  • Check direct and transitive dependencies, lockfiles, deployed artifacts, and container images.
  • Investigate whether affected LiteLLM artifacts were installed or executed.
  • Rotate credentials and investigate possible access if a compromised artifact may have exposed them.
  • Record what was checked, what could not be checked, and the evidence supporting each conclusion.

Longer term, integrate AI-assisted security review into the normal development workflow, verify high-impact findings, and keep a human responsible for approving and validating changes. An agent is not a substitute for security engineering, and a report is not proof that a system is safe. Used with evidence, guardrails, and expert review, agents can help teams cover more ground and respond more consistently.

The question is no longer whether AI will be part of vulnerability research. It already is. The important question for defenders is how to integrate it into their workflows with the same discipline they apply to any other security tool.

If your team needs help assessing exposure, investigating a vulnerability, or building a repeatable AI-assisted security workflow, you can contact me through LinkedIn to discuss the work.

Explore Ultrapowers and the Vulnerability Hunter workflow.

Sources and further reading