A new open-source tool called DockSec is tackling a frustrating problem in container security: developers get long lists of vulnerabilities but lack practical guidance on fixing them. The project combines three established security scanners with artificial intelligence powered by local language models, including support for self-hosted models through Ollama, to deliver line-by-line remediation advice instead of just alerts.

What's the Real Problem DockSec Solves?

Container security tools today fall into two camps, according to Advait Patel, the creator of DockSec. Pure scanners like Trivy and Grype excel at finding vulnerabilities but leave developers overwhelmed. Enterprise platforms like Prisma Cloud and Aqua offer better guidance but come with significant cost and operational complexity. DockSec targets the space between these two worlds.

"The workflow gap between 'scanner found something' and 'developer fixes it.' That gap is where most container vulnerabilities go to die," said Advait Patel, creator of DockSec.

Advait Patel, Creator of DockSec

Patel described a common scenario: a team receives a scan report listing 200 CVEs (Common Vulnerabilities and Exposures), and developers either ignore the findings or spend hours researching each one individually. Many issues end up filed under "accepted risk" and forgotten. DockSec changes this dynamic by providing specific, actionable remediation.

How Does DockSec Actually Work?

The tool runs three established container security scanners, Trivy, Hadolint, and Docker Scout, against a developer's Dockerfile and container image. It correlates findings across all three tools, generates a security score from 0 to 100, and then uses an AI language model to propose line-specific fixes.

What makes DockSec distinctive is its flexibility in choosing an AI backend. The tool supports four options:

• OpenAI: Uses OpenAI's cloud-based language models for explanations and remediation suggestions.
• Anthropic: Integrates Claude, Anthropic's language model, for AI-powered analysis.
• Google Gemini: Leverages Google's Gemini model for vulnerability explanation.
• Local models via Ollama: Runs self-hosted language models on your own infrastructure, requiring no API keys or external service calls.

The local Ollama option is particularly significant for teams prioritizing privacy or operating offline. DockSec includes a scan-only mode that operates entirely offline and requires no API key, making it accessible to developers without cloud service subscriptions.

The output format reflects DockSec's focus on practical fixes. Instead of generic warnings, the tool delivers reports stating something like "line 14 of your Dockerfile is the problem, here is the corrected version, here is why it matters in your specific image." Reports export to multiple formats including HTML, PDF, JSON, CSV, and Markdown.

How to Get Started with DockSec for Your Container Security

• System Requirements: DockSec requires Python 3.12 and is distributed under the MIT open-source license, making it free to use and modify.
• Choose Your AI Backend: Decide whether to use cloud-based models (OpenAI, Anthropic, Google) or self-host with Ollama for complete privacy and offline operation.
• Run Against Your Containers: Execute DockSec against your Dockerfile and container images to get a 0-100 security score with specific remediation guidance for each vulnerability.
• Export and Share Reports: Generate reports in your preferred format and share with your team or security auditors for governance and compliance documentation.

Can General-Purpose AI Tools Like Copilot Replace This?

Patel acknowledged the overlap between DockSec and general-purpose coding assistants like GitHub Copilot, Cursor, and Claude Code. He conceded that developers will eventually ask these tools "is this Dockerfile secure?" and receive reasonable answers most of the time. However, he identified two reasons why a dedicated security layer remains relevant.

Patel

The first is deterministic scanning. While a coding assistant can flag obvious issues like running containers as root, awareness of specific CVEs in base-layer libraries depends on a scanner having identified them. Patel cited OpenSSL 1.1.1k as an example of a vulnerability that requires actual scan data to surface, not just general knowledge.

"'Copilot said it was fine' does not survive an auditor's first question," explained Patel. "The dedicated layer exists because security has to live inside a governance envelope that general-purpose tools are not built for."

Advait Patel, Creator of DockSec

The second defense is governance. Security decisions need to be auditable and defensible. A general-purpose AI assistant cannot provide the documented, deterministic scan results that auditors and compliance teams require. DockSec's dedicated approach creates an audit trail that satisfies governance requirements.

What's the Competitive Landscape?

Patel identified the real competitive risk as coming from established container security platforms. Companies like Snyk, Aqua, Sysdig, and Prisma Cloud could bundle equivalent AI-powered reasoning capabilities into their existing licenses and offer them as a checkbox feature. However, DockSec's target audience is developers operating without a platform deployment and without a dedicated security budget line.

The project's roadmap includes several planned features to expand its capabilities. Upcoming additions include Docker Compose multi-service scanning, Kubernetes manifest analysis, a GitHub Action for automated pull request review, and custom security policy enforcement.

DockSec is available for free on GitHub as an OWASP Incubator Project. The tool represents a shift toward making security expertise accessible to individual developers and small teams who lack the resources for enterprise security platforms but need more than basic vulnerability lists.