Mend AppSec

Determines whether a vulnerable function is actually reachable and called in the codebase: not merely present in the dependency tree. Reduces actionable CVEs to those with real exploit paths; requires static code analysis on top of dependency scanning.

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.

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

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.

Imports or generates Vulnerability Exploitability eXchange documents asserting whether a known CVE actually affects a given product in its deployed context. Reduces false positives in downstream consumers of SBOMs.

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.

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.

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.

Risk context for open-source dependencies including reachability, exploitability, and upgrade impact.

Detection and provenance tracking of AI and ML components, models, and LLM usage within the software supply chain.

Governs third-party software consumption to apply consistent software supply chain security policy.

Live visibility into code, components, pipelines, and developer activity across the software development lifecycle.

Deep analysis of binaries and packages to detect tampering, malware, and hidden threats beyond manifest-based scanning.