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HEAD-TO-HEAD COMPARISON
Heeler vs JFrog.
Agentic Development Security — an end-to-end platform that prevents, fixes, and operates on a runtime context engine — versus JFrog's Artifactory-anchored security portfolio (Xray, Advanced Security, Runtime, Curation).
EXECUTIVE SUMMARY
Both detect. Only one models the running service.
The two overlap on detection — SCA, SAST, secrets, contextual analysis, PR scanning. Heeler leads or matches across that overlap and pulls ahead on the runtime story: its own cloud/runtime, business, and ownership context, the endpoint and internet-exposure map, and the prioritization, CI-proven remediation, workflows, and verified closure that ride on it — none of which JFrog computes.
JFrog's security is Artifactory-anchored: Xray SCA and binary scanning, an Advanced Security add-on (SAST, secrets, contextual analysis, IaC), a separate eBPF Runtime product, and Curation's registry firewall — deep on the artifact and registry surface, but license-gated by bundle and blind to where code actually runs.
Scope here is code security. JFrog's registry-native binary scanning and Curation firewall are real strengths on that surface — this page is about the security engine, not artifact management.
THE FUNDAMENTAL DIFFERENCE
Four SKUs on a registry, or one runtime-aware context engine.
Both detect SCA/SAST/secrets, both gate PRs, both ship a CLI, IDE tooling, and an MCP for AI assistants. The difference is what each knows about your environment when it acts — and where the runtime picture comes from: an eBPF sensor watching the workload for JFrog, a sensor-less cloud/runtime model for Heeler. Scope here is code security.
JFROG
Artifactory-anchored security portfolio
Scan the artifacts and curate the registry.
- →Xray SCA + binary scanning native to Artifactory (OS packages, container images, build-info), plus LLM/AI-model artifact scanning
- →Advanced Security add-on (Enterprise X/+): SAST, secrets, contextual analysis, IaC/misconfig, GitHub-Actions pwn-request scanner
- →Curation (pre-download registry firewall) and Runtime (Kubernetes eBPF monitoring) as separate products; Frogbot, CLI, IDE plugins
- →Deep on the artifact/registry surface, but bundle-gated; no cloud-topology, internet-exposure prioritization, endpoint discovery, SLO, or runtime-verified closure
HEELER
Modeled around the running service
Prevent, Fix, and Operate on one runtime-aware model.
- →One context engine across six dimensions (code, cloud/runtime, business, ownership, threat, agent), sensor-less and read-only — runtime exposure is a native input
- →Its own detection — SAST, build-graph SCA (14 ecosystems), validated secrets, agent-file governance — plus an MCP server, Agent Skills, and a CLI
- →Prioritizes by runtime reachability and internet exposure; remediation is built in a sandbox and repaired in your CI until green
- →Guardrails gate new risk at the PR; workflows operate to a runtime-verified close with per-finding SLOs
VERDICT FRAMEWORK
Side-by-side, with a verdict per row.
Four states. Heeler-leaning where Heeler advances; explicit when JFrog leads; honest about parity.
●
Heeler advantage
Heeler delivers a capability JFrog doesn't, or in a way that changes outcomes.
◐
Heeler edge
Both deliver the capability. Heeler's implementation is materially better on a verifiable dimension.
✓
Parity
Both products deliver the capability comparably.
○
JFrog advantage
Explicit signal that JFrog leads on this row.
Scorecard — 20 capabilities, scoped to code security
| Section | ● Heeler advantage | ◐ Heeler edge | ✓ Parity | ○ JFrog advantage |
|---|---|---|---|---|
| Prevent | – | 2 | 1 | 1 |
| Fix | 3 | – | – | – |
| Operate | 5 | 4 | 5 | – |
| Total | 8 | 6 | 5 | 1 |
| Capability | Heeler | JFrog | Verdict |
|---|---|---|---|
| Prevent · stop risk before and as it enters | |||
| Security at AI code generation | Heeler's MCP server hands the coding agent its runtime-aware context as it writes — 30+ read-only tools exposing live SAST, SCA, and secrets findings, endpoint exposure, runtime and deployment risk, guardrail status, and SLO pressure, plus named secure-development prompts — and can trigger Heeler's validated remediation with the right scope. Drop-in Agent Skills and the CLI cover the same surface locally, for Claude Code, Cursor, Codex, and VS Code. | JFrog's Local SAST MCP (jf source-mcp) exposes SAST findings and code-intelligence tools to Copilot and Cursor (gated to the Unified / Ultimate Security bundle), alongside IDE plugins (VS Code, JetBrains, Cursor, Windsurf — Advanced Security features require Enterprise X/+) and snippet-origin detection. Scoped to SAST findings and dependency snippets, not runtime-aware whole-surface context. | ◐Heeler edge |
| CLI / local developer scanning | The heelercli runs local scans across the full surface — SAST, SCA, secrets (offline validation), license, and malicious-package checks — with non-zero exit for pre-commit and CI, SARIF output, and policy-as-code. | The JFrog Security CLI (jf scan, jf docker scan) and IDE plugins (VS Code, JetBrains, Visual Studio, Eclipse, Cursor, Windsurf) run local SCA, SAST, secrets, and IaC scans; the advanced-security engines require an Enterprise X/+ subscription. | ✓Parity |
| PR guardrails & policy enforcement | Block / Warn / Observe guardrails post a native status check on GitHub, GitLab, Bitbucket, and Azure DevOps and gate PRs on new dependency vulnerabilities, dependency hygiene (unpinned, minimum-age, compromised), SAST, secrets, and SLO breaches. Two things set them apart: a guardrail can be scoped by runtime context — enforce only on Tier-1, production, or internet-facing services — and it ships a validated fix committed straight into the PR branch. Block relies on SCM branch protection to enforce. | Frogbot gates pull requests (SCA, SAST, IaC, secrets) and can block the merge, while Xray policies & watches fail builds, block downloads and release-bundle promotion, and open Jira tickets. Powerful policy/watch authoring, but no runtime/exposure scoping, and Frogbot's autofix is dependency-only and runs on full-repository scans, not inside the PR. | ◐Heeler edge |
| Install-time / registry package firewall | Not offered as a registry firewall. Heeler connects to registries read-only to analyze images and first-party packages, and improves install-time posture through the package manager itself — its Package-Manager Cooldown drives each manager's native minimum-release-age (npm, pnpm, Yarn, Bun, uv, pip, Deno), committed to repos and set globally on laptops, refusing too-new versions at install — plus PR guardrails and CLI scanning. It doesn't proxy the registry to block downloads. | JFrog Curation blocks risky packages pre-download at Artifactory remote repositories — policy-driven on malware, CVEs, license, and immature/aging packages, with block or dry-run modes and Compliant Version Selection that auto-returns the highest policy-passing version. Package Traffic Controller reroutes public traffic through Artifactory via SASE (Zscaler/Netskope/Cloudflare); Federation is Enterprise+. | ○JFrog advantage |
| Fix · resolve findings — code and dependencies | |||
| SAST autofix | Deterministic, strategy-matched transforms (Parameterize / Escape / Allowlist / Path-Normalize) anchored to the exact source-to-sink flow, precomputed per finding on every scan, confidence- and effort-scored, and delivered as PRs validated the same way as dependency fixes (sandbox build + CI repair). Findings needing an architectural change get written guidance instead. | JFrog provides SAST remediation guidance with each finding, and through the Local SAST MCP an AI assistant can be prompted to apply a fix; there is no automated SAST-fix PR pipeline. | ●Heeler advantage |
| SCA autofix (upgrades) | Deterministic, multi-signal upgrade selection over the resolved graph — Heeler picks the version that clears the most risk with the least disruption (no new known vulnerabilities, proven adoption, cleared minimum-age cooldown), and for transitive vulns finds the smallest bump to the direct ancestor you control (npm, Go, Cargo). Every fix is built in a sandbox and repaired through your CI until green before merge. Auto-fixable across Java, Go, JavaScript, Python, and C#. | JFrog Advanced Remediation (Xray Remediation) computes upgrade suggestions — CVE-based or component-based, in-lock override vs direct upgrade, with Quickest-Fix / Least-Vulnerable strategies — and Frogbot opens dependency-bump PRs (on full-repository scans only). It requires the JFrog Catalog and doesn't build or test the change itself. | ●Heeler advantage |
| Validated, merge-ready fixes (build + CI repair) | Heeler validates every fix twice: a real build in an isolated sandbox with your project's toolchain before the PR (mvn/Gradle, npm/pnpm/Yarn, pip/uv/Poetry, go build, dotnet), then — once the PR is open — it runs your CI and repairs its own build/test failures with up to five follow-up commits until green, or hands off with an explanation. Draft PR if the sandbox build didn't pass; human review, no auto-merge. | Frogbot opens a dependency-bump PR (SCA only, full-repository scans) and Advanced Remediation suggests upgrade paths; neither builds the project, runs tests, or repairs CI — that validation is left to your pipeline and review. | ●Heeler advantage |
| Operate · detect, prioritize, and run the program | |||
| SAST detection | Path-aware, interprocedural source-to-sink taint analysis across functions and files — plus structural (Graphscan) and heuristic methods — from Heeler's own engine, with detection rules adapted per codebase, automatic reachability/exposure triage, and step-by-step data-flow proof. Included by default, not an add-on. | JFrog's SAST engine (Advanced Security, Enterprise X/+) performs cross-file, cross-function data-flow analysis locally with a fingerprint algorithm that keeps ignores stable, mapped to CWE/OWASP with remediation guidance, across PHP, Python, JS/TS, Java, C#, C/C++, Go, and Rust. | ◐Heeler edge |
| Dependency vulnerability (CVE) detection | Build-graph SCA across 14 ecosystems (no build changes) — direct, transitive, first-party, and bundled dependencies — matched with CVSS, EPSS, and known-exploited signals, plus GitHub Actions as a first-class ecosystem. | Xray scans source and binaries against the JFrog vulnerability DB (enriched by JFrog Security Research), across a broad set of ecosystems, with license and operational-risk detection; native binary scanning in Artifactory extends coverage to OS packages and container images. | ✓Parity |
| Supply-chain attack & malicious-package detection | Heeler detects compromised/malicious and typosquatted packages, unmaintained projects (OSSF Scorecard), too-new versions (minimum-age), unpinned versions, and downgrades — and treats the CI/CD supply chain as first-class, scoring GitHub Actions on pin status, publisher trust, and provenance with integrity checks (impostor commits, typosquats, abandoned sources) and workflow attack-path analysis. | JFrog Security Research computes a maliciousness score on new public packages (obfuscation, dynamic eval, download-and-execute, exfiltration, plus typosquatting and dependency-confusion), continuously for npm/PyPI/OpenVSX/Hugging Face and on-demand elsewhere, and ingests the OpenSSF malicious-packages feed; a static GitHub Actions scanner flags the pwn-request pattern. | ✓Parity |
| Secrets detection & validation | First-class secrets: full git-history scanning across all branches with commit attribution, active live validation (read-only calls to the issuing provider, including cloud, SaaS, AI, and datastore credentials; an active secret is weighted as maximal impact), offline structural validation, and 700+ rules across 20+ languages. | JFrog secrets detection (Advanced Security) scans source and binary artifacts with structured, entropy, and certificate detectors and validates tokens against the provider — but validation is public-SaaS-only (github.com / gitlab.com; roughly half of listed providers, excluding OpenAI/Anthropic/datastores), and full git-history scanning is not documented. | ◐Heeler edge |
| Dependency reachability & noise reduction | Heeler treats a dependency vuln as reachable only when two independent checks agree: runtime reachability (the library is loaded in a running deployment) and static call-graph reachability to the vulnerable functions across six ecosystems (Go, Java, Python, JS/TS, Rust, Ruby). Unreachable findings are auto-mitigated; elsewhere it uses runtime reachability and errs toward reachable. | JFrog Contextual Analysis determines CVE applicability with per-CVE static scanners built by JFrog Research (1,600+ high-profile CVEs) that trace how first-party code uses the library; call-chain Transitive Dependency Analysis is Ultimate-Security-gated, and it's Advanced Security (Enterprise X/+). Static, per-CVE coverage rather than a general call graph. | ✓Parity |
| Runtime-aware prioritization | Heeler Risk (Urgent / Plan / Defer) ranks SAST and SCA findings by real exposure. For SCA, a vuln must clear a gate: reachable (runtime-loaded, plus a call-graph path where supported) AND internet-exposed (an active deployment on internet-reachable compute) AND not mitigated — fail any and it Defers, however high the CVSS. For SAST, exposure is path-driven from an internet-facing entry point, behind auth, touching sensitive data. Both are weighted by service tier and threat (KEV/EPSS; CWE class), with a per-finding SLO, re-scored continuously. | JFrog prioritizes by Contextual-Analysis applicability, CVSS/JFrog Research severity, fix availability, and — with Runtime Impact sensors — whether the package is loaded in memory, plus “blast radius” (how many running images use it). It has no internet-exposure, cloud-deployment-topology, or service-tier signal driving priority. | ●Heeler advantage |
| Cloud, runtime & business context | Heeler builds its own model of where code runs — containers, VMs, and serverless fingerprinted, each deployment mapped to the exact running changeset, internet-accessibility auto-detected, service tier (1–4) and prod/non-prod classified, service-to-service and identity edges resolved, and ownership resolved to a RACI — all read-only and sensor-less, with no agent to deploy and no separate add-on. This context is what makes exposure, prioritization, and routing real. | JFrog gets a runtime signal only by deploying its separately-licensed Runtime add-on — eBPF sensors on every Kubernetes node (Runtime Impact; Linux/K8s only, Windows unsupported) that watch processes, file, and kernel activity, flag image-integrity tampering, and confirm a package is loaded in memory. Even then there is no cloud asset inventory, deployment-topology graph, internet-exposure, service-tier, or ownership model; cloud misconfiguration is scanned only against Terraform state stored in Artifactory. Runtime context requires an agent and another SKU. | ●Heeler advantage |
| Endpoint & API attack-surface discovery | Heeler enumerates every endpoint from source — REST, GraphQL, gRPC, SOAP, and WebSocket, plus the MCP tools/resources/prompts and Spring Boot actuator routes your code exposes — capturing each one's authentication posture and computing internet-accessibility from deployment topology, so your externally reachable, unauthenticated surface is inventoried with no runtime agents or traffic capture. | Not offered. JFrog has no application endpoint or API discovery — its SAST identifies routes internally for taint analysis, but there is no endpoint or attack-surface inventory. | ●Heeler advantage |
| AI supply-chain security | Heeler detects every MCP server your code exposes across seven languages and catalogs the tools, resources, and prompts each exposes to agents, its transport, and network exposure (so a Public, unauthenticated MCP transport surfaces as internet-reachable attack surface), and governs agent instruction/config files with 0–100 risk scoring (see below). | JFrog scans ML model artifacts for malicious code (Hugging Face, Pickle/PyTorch/etc.) and maintains an AI Catalog / Shadow-AI inventory; it scans AI-agent skill packages (SKILL.md uploaded to an Artifactory Skills repo) for prompt-injection and exfiltration via a YARA + LLM red-team pipeline, and does snippet-origin detection — model- and Artifactory-package-anchored. | ✓Parity |
| Agent-file detection & governance | A dedicated repo-level inventory scores every agent instruction/skill/subagent/hook/MCP-config file (CLAUDE.md, AGENTS.md, .claude/skills, .cursor, .mcp.json, .codex) 0–100 across Static, LLM-intent, and External-reference risk, with a Malicious / Suspicious / Benign verdict, across Claude, Cursor, Gemini, Codex, and OpenCode — with a CI-gating API and a workflow trigger. | JFrog scans AI-agent skill packages (SKILL.md zips uploaded to an Artifactory Skills repo) for malicious behavior via YARA and an LLM red-team pipeline (beta, AI Catalog Security bundle); it does not inventory or score repo-level agent-instruction/config files such as CLAUDE.md or .mcp.json. | ◐Heeler edge |
| SBOM & dependency inventory | A live dependency inventory (with OpenSSF hygiene scoring) and CycloneDX SBOMs at five scopes — including runtime SBOMs per running service and per deployment, built from real runtime context — alongside global, application, and repository scopes. | JFrog exports CycloneDX (1.3–1.6) and SPDX, plus CBOM (cryptography BOM, Advanced Security), SBOM import, base-image detection, and transitive SBOM — all from source, build, and binary scans, with no runtime SBOM of running services. | ◐Heeler edge |
| Automated response workflows & routing | A standing when-this-then-that engine across the whole estate: one trigger (new finding, fix available, compromised dependency, new secret, at-risk agent file, SLO expiring, PR merged with unresolved guardrail violations, and more) → AND-combined conditions → up to three chained actions that share context. Actions span messaging (Slack/Teams/Google Chat/email), ticketing (Jira/Linear/GitHub/Shortcut, de-duplicated), and domain actions (Fix with Heeler Agent, Update SLO Override, Generate SBOM, Webhook), routed to the owning team by RACI. Built in plain English, with suggested-workflow gap detection. | JFrog automates response through Xray policies & watches (fail build, block download/promotion, notify), webhooks (Xray and Curation), and Jira ticketing — policy/watch- and webhook-driven, without a general trigger → condition → chained-action workflow engine, owner-RACI routing, or fix/SLO actions inside the workflow. | ●Heeler advantage |
| SLOs & runtime-verified closure | Every finding carries a per-band SLO clock; SLO-expiring events trigger workflows, and an Update SLO Override action can extend a due date automatically. Findings run Active → Fixed → Deployed, and closure is runtime-verified — a fix reaches Deployed only once every running deployment confirms the vulnerability is actually gone, not merely merged. | JFrog closes findings when a subsequent scan no longer detects them (linked Jira issues auto-resolve) and can show a CVE “Validated in Runtime” with a declining trend — but there is no SLO engine and no formal runtime-verified closure (only a Curation catalog-population SLA). | ●Heeler advantage |
WHERE HEELER ADVANCES
Where a runtime-native engine beats a registry-anchored portfolio.
Each maps back to the context engine — not a SKU bolted onto a registry.
01
Native runtime and cloud context, sensor-less
To get any runtime signal, JFrog has you deploy its separately-licensed Runtime add-on — eBPF sensors on every Kubernetes node (Linux only). Heeler builds its own model of where code runs from cloud APIs and Kubernetes inventory — read-only, sensor-less, no add-on: deployment-to-changeset mapping, service edges, service tier, ownership, and internet-accessibility — and it drives prioritization, remediation, and closure.
02
Prioritize by internet exposure, not just applicability
JFrog narrows CVEs by Contextual-Analysis applicability and, with sensors, whether code is loaded in memory — useful, but it has no internet-exposure, cloud-topology, or service-tier signal. Heeler gates a dependency finding on reachable AND internet-exposed AND not-mitigated, and ranks SAST by a traced path from an internet-facing entry point — so a live, exposed finding outranks a dormant one with the same CVSS.
03
Remediation proven in your CI, not just suggested
JFrog's Advanced Remediation suggests upgrade paths and Frogbot opens dependency-bump PRs on full-repo scans — but neither builds or tests the change. Heeler builds every fix in an isolated sandbox and repairs its own CI failures until green — for code and dependencies — before a human merges it, opening a draft PR if the build didn't pass.
04
Operate to a runtime-verified close, with SLOs
Heeler runs a chained workflow engine with RACI owner-routing, per-band SLO clocks, and Active to Fixed to Deployed closure confirmed from the running deployment. JFrog operates through policies, watches, webhooks, and Jira tickets — real automation, but no general workflow engine, no SLO engine, and no runtime-verified closure.
05
One engine across the surface, not four SKUs on a registry
JFrog's security is Xray plus an Advanced Security add-on (Enterprise X/+), a separate Runtime product, and Curation — much of it anchored on Artifactory and bundle-gated. Heeler runs prevent, detect, prioritize, fix-and-prove, and operate across SAST, SCA, secrets, supply chain, cloud/runtime, and agent files on one context engine, sensor-less — at machine speed for the AI SDLC.
See Heeler on your codebase.
A demo runs Heeler against your real repos and cloud — detection across the surface, the runtime map, the prioritization, the validated remediation PRs, and the agent-file catalog. If JFrog is scanning your artifacts and curating your registry today, we'll show what native runtime context adds on top.
