Snyk AI Security Platform

Identifies packages with known-malicious behavior (typosquatting, dependency confusion, backdoored releases), distinct from packages with CVEs in legitimate code.

Traverses the full dependency graph to surface CVEs in indirect dependencies, packages required by your direct dependencies. Direct-only scanning misses the majority of vulnerable code paths in modern polyglot projects.

Opens PRs with upgraded dependency versions that resolve CVEs. Quality differentiation is whether the fix resolves transitive chains or only direct dependencies, and whether the PR is merge-safe without manual review.

Blocks or flags PRs in CI/CD pipelines based on policy-defined thresholds, configurable by severity, CVSS score, exploitability, fix availability, or CVE age. Prevents vulnerable code from merging without requiring zero-tolerance policies.

Scans images stored in registries (ECR, GCR, Artifact Registry, Docker Hub), for vulnerable OS packages and application dependencies at push time or on schedule, without requiring a running container.

Determines whether a vulnerable function is actually reachable and invoked, not merely present in the dependency tree, cutting actionable CVEs down to those with real exploit paths. Delivered either statically, by call-graph analysis layered on dependency scanning, or at runtime, by instrumenting the workload to observe which components actually execute.

Exports the dependency inventory as a machine-readable Software Bill of Materials in SPDX or CycloneDX format, consumable by downstream vulnerability scanners, compliance tools, and procurement workflows.

Identifies OSS licenses in the dependency tree and flags conflicts with the project's target license or policy (GPL contamination, copyleft obligations, export-controlled components). Separate from vulnerability detection.

Identifies hardcoded credentials, API keys, tokens, and private keys in source files. Operates on the repository and commit history, not at runtime.

Defines open source policies (banned licenses, blocked packages, version floors, severity gates) as version-controlled rules applied automatically at scan time across repositories.

Prioritizes dependency vulnerabilities using exploitation signals such as EPSS probability and the CISA Known Exploited Vulnerabilities catalog, ranking findings by real-world exploitation likelihood rather than CVSS severity alone.

Scans infrastructure-as-code definitions across Terraform, CloudFormation, ARM and Bicep, Pulumi, Kubernetes YAML and Helm charts against security and compliance policies before deployment, so misconfigurations are caught in code rather than in production. Framework coverage and check depth per framework vary across products.

Integrates as a productized step in CI/CD pipelines and stops or fails a build when infrastructure-as-code findings exceed a policy-defined risk threshold, with severity thresholds and documented exceptions, so insecure infrastructure cannot reach deployment.

Detects drift between the infrastructure-as-code definition and the resource actually deployed in the cloud, including out-of-band console changes, and remediates it by regenerating the IaC script or reverting the resource, keeping code as the source of truth.

Lets teams author and version their own infrastructure-as-code policies in a policy language such as Open Policy Agent Rego or a vendor rules format, alongside prebuilt policy packs mapped to CIS and other benchmarks, so organization-specific guardrails are enforced with the same tooling as standard checks.

Surfaces infrastructure-as-code findings inside the developer's IDE and as inline pull-request comments with suggested fixes, so misconfigurations are corrected at authoring time rather than after a pipeline failure.