# Container security: growing risks, challenges, and how AI can help **URL:** https://mazehq.com/learn/container-security **Date:** 2026-10-15 ## What is container security? Container security is the practice of protecting containerized applications and their supporting infrastructure throughout the software lifecycle. It covers container images, registries, runtimes, orchestration platforms, hosts, networks, and application dependencies. Security controls should start during development, when teams can scan container images for known vulnerabilities, exposed secrets, and unsafe configuration before deployment. Using trusted base images, limiting installed packages, and signing images can also reduce supply chain risks. Runtime security focuses on limiting what a compromised container can do. Common controls include running containers as non-root users, restricting Linux capabilities, using read-only file systems, applying network policies, and enforcing resource limits. Runtime monitoring can detect unexpected processes, file changes, or network connections. AI is increasingly used to take on the investigation work that container security has always left to people. Given unlimited time, a security engineer would check whether a vulnerable package is ever loaded, how the workload is exposed, and which controls stand in the way before deciding whether a finding matters. AI can gather that context across vulnerability, configuration, runtime, and deployment data, decide which findings can actually be exploited in your environment, and recommend a fix. It can also trace vulnerable components from source code through container images to deployed workloads. ## Why is container security important? ### Containers share the host operating system kernel Unlike virtual machines, containers do not run separate operating system kernels. Multiple containers typically share the kernel of the same host. This architecture reduces overhead, but it also means that a kernel vulnerability or container escape can affect workloads outside the compromised container. Teams can reduce this risk by keeping hosts patched, running containers with minimal privileges, and removing unnecessary Linux capabilities. Additional isolation mechanisms such as seccomp, AppArmor, and SELinux can restrict the system calls and resources available to container processes. ### Container images can introduce vulnerable dependencies Container images often include operating system packages, language runtimes, libraries, and application dependencies. A vulnerability in any of these components becomes part of every container created from the image. Older base images can also retain vulnerabilities even when the application itself is current. Image scanning can identify known vulnerabilities before an image reaches production. Teams should use trusted minimal base images, pin dependency versions where appropriate, rebuild images when security updates become available, and prevent images with unacceptable vulnerabilities from being deployed. ### Misconfigurations can expose containerized workloads Unsafe configuration can weaken container isolation or expose services unnecessarily. Examples include running containers as root, mounting sensitive host directories, enabling privileged mode, exposing management interfaces publicly, or granting excessive Kubernetes permissions. Configuration policies can detect or block these settings during development and deployment. Teams should apply least-privilege access, restrict host mounts and network exposure, protect secrets, and enforce security standards through admission controls and infrastructure-as-code checks. ## What are the main container security risks? ### Container security risks at a glance RiskPrimary concernsCommon mitigations**Vulnerable container images**Known CVEs are inherited by every container created from the imageContinuous scanning, deployment gates, regular rebuilds**Vulnerable open-source dependencies**Direct and transitive packages introduce exploitable flawsSoftware composition analysis, SBOMs, dependency updates**Outdated base images**Old operating system and runtime components retain patched vulnerabilitiesMaintained base images, minimal images, controlled rebuilds**Malicious or untrusted images**Compromised images can introduce malware or backdoorsTrusted registries, image signing, provenance verification**Container misconfigurations**Unsafe runtime settings weaken isolation or expose servicesConfiguration scanning, policy-as-code, admission controls**Excessive container privileges**Broad capabilities increase the impact of compromiseCapability reduction, seccomp, AppArmor, SELinux**Containers running as root**Root execution increases the consequences of escapes and misconfigurationNon-root users, security contexts, privilege restrictions**Exposed secrets and credentials**Embedded credentials can be extracted from images or runtime environmentsRuntime secret injection, short-lived credentials, rotation**Insecure container registries**Attackers can replace or upload images into the software supply chainStrong authentication, RBAC, immutable artifacts, audit logging### Vulnerable container images Container images package application code together with operating system libraries, runtimes, utilities, and dependencies. A vulnerability anywhere in this stack can become part of every workload created from the image. Reused base images can multiply the same exposure across dozens or hundreds of applications, making image-level weaknesses a significant source of systemic container risk. **Risks:** - Known vulnerabilities may remain present in production images long after patches become available. - Shared base images can spread the same vulnerable component across many workloads. - High-severity CVEs can be difficult to prioritize when scanners generate large numbers of findings. - Images that passed an earlier scan can become vulnerable when new CVEs are disclosed. **Mitigations:** - Scan images during builds, before deployment, and continuously after deployment. - Prioritize findings using severity, exploitability, runtime exposure, and business impact. - Block images that exceed defined vulnerability thresholds from reaching production. - Rebuild and redeploy images from patched sources instead of modifying running containers. ### Vulnerable open-source dependencies Modern applications frequently depend on large numbers of open-source libraries, frameworks, and packages. Many of these dependencies also bring in transitive dependencies that developers did not select directly. A vulnerable component deep in this dependency tree can expose the application even when its own source code is secure. **Risks:** - Developers may not know that vulnerable transitive dependencies are included. - A single vulnerable library can appear across many images and applications. - Unsupported or abandoned packages may stop receiving security fixes. - Updating a vulnerable dependency can introduce compatibility or application failures. **Mitigations:** - Use [software composition analysis](https://mazehq.com/learn/software-composition-analysis-how-it-works-6-best-practices) to identify direct and transitive dependencies. - Generate SBOMs so each image has a complete component inventory. - Remove libraries and packages that the application does not require. - Test dependency upgrades before rebuilding and redeploying production images. ### Outdated base images Base images provide much of the operating system and runtime environment used by application containers. When teams continue building from an old base image, they also continue inheriting vulnerabilities that may already have been fixed upstream. Widely reused base images can therefore become a common source of exposure across an entire container estate. **Risks:** - Old operating system packages can retain publicly known vulnerabilities. - Reused base images can propagate the same weaknesses across applications. - Unmaintained images may no longer receive security patches. - Digest-pinned images remain unchanged until teams deliberately rebuild them. **Mitigations:** - Select actively maintained and trusted base images. - Prefer minimal images containing only required runtimes and packages. - Monitor approved base images for security updates and new releases. - Trigger controlled application rebuilds when important base-image fixes become available. ### Malicious or untrusted images Container images are executable software artifacts, which makes untrusted images a direct software supply chain risk. Public registries may contain intentionally malicious images, altered copies of legitimate projects, or similarly named repositories designed to trick developers into downloading the wrong artifact. **Risks:** - Images can contain malware, backdoors, cryptominers, or credential-stealing code. - Typosquatting and impersonation can cause teams to pull unintended images. - Compromised publisher accounts can distribute altered versions of trusted images. - Unsigned artifacts make it difficult to verify who produced an image. **Mitigations:** - Restrict deployments to approved registries, repositories, and publishers. - Scan third-party images before allowing them into trusted environments. - Sign internally produced images and verify signatures before deployment. - Validate provenance so deployment systems can confirm an image came from an approved build. ### Container misconfigurations Containers can be secure at the image level but still become vulnerable through unsafe deployment or runtime configuration. Dockerfiles, Kubernetes manifests, orchestration settings, filesystem permissions, exposed ports, and host integrations all affect the security boundary around a running container. **Risks:** - Sensitive host directories may be mounted into workloads. - Unnecessary ports or management interfaces may be exposed publicly. - Writable filesystems and unrestricted networking can expand attack options. - Runtime settings may drift from configurations reviewed during development. **Mitigations:** - Scan Dockerfiles, manifests, and infrastructure-as-code before deployment. - Use policy-as-code to define required container security settings. - Apply admission controls that reject workloads violating mandatory policies. - Continuously assess deployed workloads for configuration drift. ### Excessive container privileges Container runtimes expose controls that determine how much of the underlying host a workload can access. Granting broad Linux capabilities, privileged mode, host devices, or sensitive mounts can significantly weaken isolation. If the application is compromised, these privileges can provide an attacker with additional paths toward the host or other workloads. **Risks:** - Privileged mode can bypass many normal container isolation controls. - Unnecessary Linux capabilities can enable sensitive kernel operations. - Host device or filesystem access can expose resources outside the container. - Excessive privileges can turn an application exploit into a host-level compromise. **Mitigations:** - Avoid privileged containers unless there is a documented requirement. - Drop all unnecessary Linux capabilities and add back only those required. - Restrict access to host devices, filesystems, namespaces, and runtime sockets. - Apply seccomp, AppArmor, or SELinux policies to constrain system access. ### Containers running as root Running as root inside a container does not automatically provide unrestricted host access, but it gives a compromised process more capabilities than a non-root process. The risk increases substantially when root execution is combined with privileged mode, host mounts, dangerous capabilities, or container runtime access. **Risks:** - Container escapes can have greater impact when the process already runs as root. - Misconfigured mounts can expose root-owned host resources. - Applications can modify more files and processes inside the container. - Privilege escalation becomes easier when additional capabilities are available. **Mitigations:** - Define dedicated non-root users in container images. - Configure Kubernetes security contexts to require non-root execution. - Disable privilege escalation where the workload supports it. - Set explicit user and group IDs instead of relying on unsafe defaults. ### Exposed secrets and credentials Containers frequently need credentials for databases, APIs, cloud services, certificates, and internal systems. Embedding those secrets into images, Dockerfiles, repositories, or deployment manifests makes them difficult to control and can expose them to anyone who can inspect the artifact or configuration. **Risks:** - Secrets can remain in earlier image layers even after later removal. - Environment variables may appear in diagnostics, process inspection, or logs. - Long-lived credentials remain useful to attackers after exposure. - Shared credentials make attribution and revocation more difficult. **Mitigations:** - Inject secrets at runtime through an appropriate secrets management system. - Scope each credential to the specific workload and resources that require it. - Prefer short-lived credentials or workload identities over static secrets. - Revoke and rotate exposed credentials immediately. ### Insecure container registries Container registries sit directly in the path between the build system and production deployment. If attackers gain permission to upload, overwrite, or replace images, they may be able to introduce malicious code without modifying the application’s source repository. **Risks:** - Stolen registry credentials can enable unauthorized image uploads. - Mutable tags can be redirected to altered artifacts. - Excessive registry permissions can allow unnecessary deletion or replacement. - Compromised CI/CD credentials can provide indirect access to the registry. **Mitigations:** - Require strong authentication and encrypted registry connections. - Apply role-based access controls to push, pull, delete, and administration operations. - Use immutable tags or image digests for production deployments. - Maintain audit logs and verify signatures or provenance before images are deployed. ## Container security across the application lifecycle ### Secure container development Container security begins with the application source code and its container configuration. Developers should avoid hard-coded credentials, unnecessary packages, insecure defaults, and dependencies from untrusted sources. Dockerfiles and orchestration manifests should follow defined security standards. Static analysis, dependency scanning, secret detection, and configuration checks can run when code is committed or during pull requests. Finding problems at this stage usually makes them easier to fix because developers can address them before an image is built and distributed. Development environments should also follow least-privilege principles. Access to source repositories, build systems, signing keys, and production resources should be limited so that a compromised developer account cannot easily affect the software supply chain. ### Container image build security The build process converts application code and dependencies into an executable container image. An insecure build pipeline can introduce vulnerable packages, malicious components, secrets, or unexpected changes even when the application source itself is secure. Builds should use trusted, maintained base images and install only required packages. Multi-stage builds can keep compilers, package managers, and other build-time tools out of the final image, reducing its size and attack surface. CI/CD pipelines should scan completed images for vulnerabilities, secrets, malware, and policy violations. Producing an SBOM and signing the resulting image can also provide evidence about its contents and origin. Build infrastructure and signing credentials should be tightly protected. ### Container registry security Registries provide storage and distribution for container images. Because deployment systems pull executable artifacts from them, unauthorized modification of a registry can provide a direct path for malicious code to reach production. Registry access should require strong authentication and role-based permissions. Developers and automated systems should receive only the permissions they require, while administrative actions such as deleting or overwriting images should be more tightly restricted. Organizations can scan stored images, retain audit logs, and use immutable image tags or digests where appropriate. Image signatures and provenance checks provide additional assurance that an artifact came from an approved build process and has not been replaced. ### Container deployment security Deployment is the point where an image becomes a running workload and receives access to networks, storage, secrets, and other infrastructure. Secure images can still create significant risk if deployed with unsafe runtime settings. Deployment policies should prevent privileged containers, unnecessary host mounts, excessive Linux capabilities, and root execution where these settings are not required. Resource limits, read-only filesystems, network policies, and appropriate security contexts can further restrict each workload. In Kubernetes environments, admission controls can automatically evaluate manifests before workloads are created. Teams can use these controls to enforce organization-wide requirements rather than depending on developers to identify every unsafe configuration manually. ### Container runtime security Runtime security focuses on containers after they begin executing. Preventive controls cannot identify every attack, especially when an attacker exploits an unknown vulnerability or uses legitimate application functionality in an unexpected way. Runtime monitoring can detect suspicious processes, unexpected file modifications, unusual network connections, privilege escalation attempts, and access to sensitive resources. Alerts should distinguish expected application behavior from activity that could indicate compromise. Isolation controls also limit the effect of successful attacks. Seccomp profiles, AppArmor or SELinux policies, Linux capability restrictions, network segmentation, and least-privilege identities can reduce what an attacker is able to access from a compromised container. ### Post-deployment vulnerability management A container that passes security checks during deployment does not remain secure indefinitely. New vulnerabilities can be disclosed in its operating system packages, application libraries, runtimes, and other dependencies after the image is already running. Organizations should continuously reassess deployed images against updated vulnerability information. An accurate inventory and SBOM can help determine which workloads contain an affected component and prioritize remediation based on severity, exploitability, and exposure. When a fix is required, teams should generally patch the source or base image, rebuild the container, repeat security checks, and redeploy it rather than modifying running containers manually. This preserves immutable deployment practices and ensures the corrected artifact can be reproduced consistently. ***Related content: Read our detailed guide to [vulnerability management](https://mazehq.com/learn/vulnerability-management-process-metrics-and-technologies).*** ## Container security vs. Kubernetes security **Container security** protects containerized applications across their entire lifecycle. It covers application dependencies, container images, registries, build pipelines, container runtimes, host systems, and running workloads. These controls apply whether containers run with Kubernetes, another orchestrator, or directly on a container runtime. **Kubernetes security** focuses specifically on protecting Kubernetes clusters and the workloads managed by them. It includes securing the Kubernetes API, role-based access control (RBAC), service accounts, secrets, admission controls, network policies, pod security settings, and communication between cluster components. The two areas overlap significantly, because container security defines many of the requirements that Kubernetes enforces. For example, container security can require workloads to run as non-root users and drop unnecessary Linux capabilities, and Kubernetes provides security contexts and admission policies that can enforce these requirements across pods and containers. The following table summarizes the differences: AspectContainer securityKubernetes security**Scope**Secures containers across the full lifecycle, regardless of where they runSecures Kubernetes clusters and workloads managed by Kubernetes**Key focus**Images, dependencies, registries, build pipelines, runtimes, hosts, workloadsAPI server, RBAC, service accounts, secrets, admission controls, network policies**Enforcement**Uses image scanning, runtime protection, least-privilege container settings, host hardeningUses Kubernetes policies, security contexts, RBAC, admission controllers, network policies**Overlap**Defines workload security requirements such as non-root users and reduced capabilitiesEnforces those requirements at the pod or cluster level## Container security challenges ### Too many vulnerability findings A single container image can contain hundreds of operating system packages and application dependencies. Scanning many images across development and production can therefore generate thousands of vulnerability findings, including duplicate CVEs inherited from shared base images and false positives for packages that are never loaded or can’t be reached. Treating every finding as equally urgent creates alert fatigue and consumes remediation resources. **How to overcome:** Teams should prioritize vulnerabilities based on severity, exploitability, workload exposure, available fixes, and the importance of the affected application. Organizations can also reduce duplicate work by identifying common vulnerable base images. Fixing and rebuilding one widely used base image may remove the same vulnerability from many application images. Because container environments change constantly, it also helps to check regularly which containers are actually running, so findings on images that aren’t deployed anywhere stop competing with live ones. ### Difficulty determining which CVEs are exploitable A scanner may report a CVE because a vulnerable package exists inside an image, but that does not necessarily mean attackers can exploit it. The affected component might never execute, the vulnerable function may not be used, or network and runtime controls may prevent access to it. This makes [vulnerability prioritization](https://mazehq.com/learn/12-vulnerability-prioritization-factors-and-why-go-beyond-them) more difficult than simply sorting CVEs by severity score. Teams need additional context, such as whether the package is loaded at runtime, whether exploit code exists, and whether the workload is exposed to potential attackers. **How to overcome:** For each finding, research what the vulnerability needs in order to be exploited, such as a specific configuration, an exposed port, or a feature that has to be turned on, and then check whether those conditions exist in the environment, including any compensating controls that would block the attack. Runtime and reachability information shows whether the vulnerable code is loaded at all. Combining this evidence with severity and business impact lets teams focus remediation on the findings that can actually be exploited. ### Short-lived and ephemeral workloads Containers are frequently created, replaced, and terminated as applications scale or deployments change. Some workloads may exist for only seconds or minutes, making them difficult to inventory and monitor with security tools designed for persistent servers. A compromised container can also disappear before an investigation begins. If security data exists only inside that container, important evidence such as process activity, network connections, and filesystem changes may be lost when it terminates. **How to overcome:** Security monitoring should collect telemetry outside individual containers and preserve it centrally. Integrating security controls with the orchestrator also helps track workloads by image, deployment, namespace, and identity rather than relying only on temporary container IDs. ## How AI is changing container security AI is changing container security by accelerating both vulnerability research and defensive analysis. It can help teams investigate weaknesses, understand attack paths, and prioritize remediation much faster than manual processes alone. ### Faster vulnerability research AI can reduce the time needed to investigate vulnerabilities in container images and the software they package. Tasks that once required hours or days of manual code review, exploit research, and defensive analysis, such as working out which package and function a CVE affects and what it takes to exploit it, can now be completed much faster. ### Broader attack surface analysis AI can examine large amounts of code and identify multiple ways a vulnerability could be exploited. It can trace execution paths, classify possible attack techniques, and identify alternatives that a manual review might miss. In a container environment, that analysis can extend from the application code through the image to the running workload, including its exposure, identities, and permissions, which allows both attackers and defenders to understand the full attack surface more quickly. ### Analysis of security controls AI can also reason about how security controls work instead of only testing whether they block a known payload. For a container, that means reading the controls around the workload, such as seccomp and AppArmor profiles, dropped Linux capabilities, network policies, and admission rules, and working out whether they would stop a specific vulnerability from being exploited or leave a gap. That analysis shows teams which vulnerabilities their existing controls already block and where a control is weaker than it looks. ### Lower barriers for attackers The same capabilities can make offensive security work easier. Attackers can use AI to reduce the expertise and effort required to develop exploits, analyze defenses, and identify possible bypasses. AI does not remove the need for human judgment, but it can compress much of the mechanical work involved in vulnerability research from days into hours, which shortens the window between a vulnerability being disclosed in a widely used base image or library and attackers being able to use it. ### Faster vulnerability triage for defenders Defenders can use AI to analyze large container vulnerability backlogs and determine which findings require the most attention. Instead of relying only on severity scores, AI can investigate whether each CVE can be exploited in a specific environment by checking configuration, exposure, and compensating controls. It can also review code for dangerous patterns and help identify vulnerabilities that may be easy to exploit but difficult to notice during manual review. ### AI-assisted detection and remediation AI can help security teams create detection rules for new attack techniques, including techniques that may not yet appear in public vulnerability databases. It can also support investigation, prioritization, and remediation, for example by tracing a container vulnerability to the base image or dependency that introduced it and suggesting the fix. This allows security teams to spend less time on repetitive analysis and more time validating findings and fixing the vulnerabilities that present the greatest risk. ***Related content: Read our detailed guide to [AI security](https://mazehq.com/learn/ai-security-development-vs-production-risks-and-how-to-mitigate-them).*** ## Container security best practices Here are some of the ways that organizations can improve their security when using containerized systems. ### 1. Continuously scan container images for vulnerabilities Container images should be scanned for known vulnerabilities when they are built and before they are deployed. Scanning should cover operating system packages, language-specific dependencies, and other software included in the image. Scanning should continue after deployment because an image considered secure today may contain a vulnerability disclosed later. Organizations can rescan registry and production images as vulnerability databases are updated and trigger remediation when new high-risk findings appear. Teams should integrate scanning into CI/CD pipelines and define policies for unacceptable risk. For example, a build might be blocked when it contains a critical vulnerability with a known exploit and an available fix. **Action items:** - Scan images automatically during CI/CD builds and before registry promotion. - Rescan stored and deployed images when vulnerability databases are updated. - Define deployment gates for critical, exploitable, or policy-prohibited vulnerabilities. - Track affected image digests so vulnerable workloads can be located precisely. ### 2. Keep images and dependencies updated Container images can accumulate vulnerabilities when base images, operating system packages, runtimes, and application dependencies become outdated. Regular rebuilding ensures applications receive upstream security fixes instead of continuing to run old software indefinitely. Teams should use maintained base images and remove packages that the application does not require. Smaller images generally contain fewer components that can introduce vulnerabilities and also make dependency inventories easier to manage. Updates should follow a controlled process, because even when automated tools detect new dependency or base-image versions, updated images should still pass application tests, vulnerability scans, and deployment checks before replacing production workloads. **Action items:** - Use maintained base images and monitor them for new security releases. - Remove unnecessary operating system packages, libraries, and build tools. - Automate dependency update detection and controlled image rebuilds. - Run tests and security checks on rebuilt images before production deployment. ### 3. Run containers as non-root users Containers should run application processes as non-root users unless elevated permissions are specifically required. Root access increases the potential impact of container escape vulnerabilities, unsafe host mounts, and other isolation failures. Images can define a dedicated user and group with only the filesystem and application permissions needed by the workload. Kubernetes security contexts can also specify user and group IDs and require containers to run as non-root. Teams should test this restriction during development because applications sometimes assume they can write to root-owned directories or bind to restricted resources. Fixing these assumptions is preferable to granting the entire container unnecessary privileges. **Action items:** - Define a dedicated non-root user and group in each production image. - Configure orchestrator policies to reject unnecessary root execution. - Set explicit filesystem ownership and permissions for application directories. - Test applications under non-root execution during development and CI. ### 4. Prioritize exploitable vulnerabilities Vulnerability scanners often produce more findings than security and development teams can remediate immediately. Prioritizing only by [CVSS score](https://mazehq.com/learn/cvss-score-components-use-cases-and-5-ways-to-go-beyond-cvss) can waste resources on vulnerabilities that are present in an image but difficult or impossible to exploit in its actual deployment. Prioritization should consider whether vulnerable code is reachable, whether a public exploit exists, and whether the workload is externally accessible. Runtime usage, available privileges, sensitive data access, and compensating security controls provide additional context, which is why the same CVE can be a low priority in a staging container and a critical one in an internet-facing production workload. This approach does not mean ignoring lower-priority CVEs permanently. Instead, it helps teams address vulnerabilities with credible attack paths first while tracking remaining findings according to defined remediation policies. **Action items:** - Combine CVSS severity with exploit availability, reachability, and runtime usage. - Prioritize externally exposed workloads and containers with sensitive access. - Include workload privileges and business criticality in remediation decisions. - Reassess deferred vulnerabilities when runtime or deployment conditions change. ### 5. Prevent privilege escalation A compromised container should not be able to gain permissions beyond those required by its application. Teams should avoid privileged containers, drop unnecessary Linux capabilities, restrict host filesystem and device access, and prevent processes from acquiring additional privileges. Kubernetes security contexts can set allowPrivilegeEscalation to false where supported by the workload. Seccomp, AppArmor, and SELinux policies can further limit system calls and access to operating system resources. Access to sensitive interfaces also requires strict control. In particular, mounting a container runtime socket inside a workload can provide extensive control over the host and other containers and should be avoided unless there is a specific, protected use case. **Action items:** - Disable privileged mode and drop unnecessary Linux capabilities. - Set allowPrivilegeEscalation to false where workloads support it. - Restrict host mounts, devices, namespaces, and container runtime sockets. - Apply seccomp, AppArmor, or SELinux profiles to sensitive workloads. ### 6. Generate and maintain SBOMs A software bill of materials (SBOM) records the software components and dependencies included in an application or container image. It helps security teams determine whether an image contains an affected component when a new vulnerability is disclosed. SBOMs should be generated automatically as part of the build process and associated with the exact image they describe. Using immutable image digests helps maintain this relationship even when image tags change. An SBOM is most useful when it stays connected to vulnerability management and deployment data. Teams can use it to identify affected workloads, investigate dependency relationships, verify software provenance, and prioritize rebuilding images when vulnerable components are discovered. **Action items:** - Generate an SBOM automatically for every production image during the build. - Associate each SBOM with an immutable image digest rather than only a mutable tag. - Store SBOMs centrally and connect them to vulnerability management workflows. - Regenerate SBOMs whenever images or included dependencies change. ## Getting container vulnerabilities under control with Maze While working with security teams, Maze’s founders watched scanners flag thousands of container vulnerabilities that nobody had the time or the context to sort through, so teams triaged by severity scores that said little about their own clusters. They built Maze so that every vulnerability gets the investigation those teams never had time to run. Maze Cloud uses AI to investigate the vulnerabilities your existing scanners find in the workloads running in your cloud, across every layer of a container, from the base image and packages you inherit, to the images in your registries and the containers you run, to the code and dependencies you write. It deploys in under five minutes with a read-only role in your cloud account and nothing to install, sees inside your clusters to map which containers are live and how each one is exposed, and then works out what context each finding needs and gathers it from your code, build, and cloud the way your best security engineer would. Maze Code brings the same investigation to your code, using AI to find the vulnerabilities in your code and dependencies, and because the two share context, investigations get sharper on both sides. Maze isn’t a runtime threat detection tool, and it runs alongside one, finding and fixing the exploitable vulnerabilities before anyone attacks them. **Key capabilities of Maze:** - **Investigate every vulnerability in your backlog:** Maze investigates every finding your scanners report, across cloud and code, instead of a sample or only the criticals, so the thousands of vulnerabilities sitting in your backlog each get a verdict with evidence rather than waiting for someone to have time. - **Visibility inside your clusters:** Maps which containers are live across EKS, ECS, and AKS, which images they run, and how each one is exposed, along with images in registries such as Docker Hub and Azure Container Registry. Because container environments change constantly, Maze regularly checks what’s running, closes findings on dead assets, deprioritizes containers that stop running, and investigates new exposure as it appears. - **Investigation with evidence:** Investigates every vulnerability, from known CVEs to zero-days, by researching what it needs in order to be exploited and checking whether those conditions exist across configuration, controls, exposure, and runtime state. Every verdict comes with the evidence and reasoning behind it. - **Exploitability-based prioritization:** On average, Maze finds that over 70% of vulnerabilities are not exploitable. Vulnerabilities that aren’t exploitable are closed with the full reasoning behind the decision. The ones that remain are ranked by how difficult they would be to exploit and how much harm they would do to the business, because the same finding can be a low in staging and a critical in production. - **Base image and layer tracing:** Traces each vulnerability to its root cause, whether a base image, a build step, or a direct or transitive dependency, and groups findings that share a fix so a single image or dependency change clears all of them. When the vulnerability comes from the base image, Maze lays out the remediation options and their tradeoffs, such as upgrading the base image or rebuilding to pull in the patch, so you can pick the fix that fits. - **Fixes where your developers work:** Writes a fix that matches your code and routes it to the developer who owns it, through your ticketing tools, as a pull request, or to coding agents such as Claude Code and Cursor, and your team stays in control of what merges. When no patch exists, Maze creates a mitigation that makes the vulnerability not exploitable. - **AI-SAST and AI-SCA:** AI-SAST runs on every pull request and across your entire codebase. AI-SCA uses AI to trace the path from an entry point to the vulnerable function in your dependencies and checks whether that code is running in your clusters. If you already run something like Dependabot, Maze investigates and deduplicates those findings too. - **Zero-day sweeps:** When a new vulnerability is disclosed, Maze picks it up in near real time and checks every affected workload across code and cloud with the same investigation, so teams know whether they’re exposed before scanners have caught up. ![Maze product screenshot](https://mazehq.com/wp-content/uploads/2026/06/Investigation-view-single-container-Rnd@2x.png)Source: [Maze](https://mazehq.com/)[Learn more about how Maze Cloud gets cloud and container vulnerabilities under control](https://mazehq.com/platform/cloud).