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Heeler vs Mobb.
Heeler is an end-to-end Agentic Development Security platform that prevents, fixes, and operates — modeled around the service across code, dependencies, runtime, and cloud — versus an autofix layer that ingests other scanners' findings. This isn't feature-for-feature; it's a category difference.
Built for the AI SDLC, not a fix step bolted on.
Heeler was built for the AI SDLC, modeled around the service — it runs the whole loop (Prevent, Fix, Operate) across SAST, SCA, secrets, supply chain, and agent files on one context engine that unifies code, dependencies, runtime, and cloud, with its own detection, runtime-aware prioritization, and remediation built and repaired in your CI until green. Mobb is an autofix layer for SAST findings: it ingests findings from a commercial scanner you run separately (or its bundled open-source Opengrep) and generates developer-reviewed code fixes — that one step, and only for SAST.
Note: the difference is category, not features.
Heeler runs the whole loop; Mobb fixes what a scanner found.
Heeler owns the full loop — prevent, detect, prioritize, fix-and-prove, and operate to closure — across SAST, SCA, secrets, supply chain, and agent files, on one context engine spanning code, dependencies, runtime, and cloud. Mobb takes SAST findings (from a commercial scanner you run separately, or its bundled open-source Opengrep) and generates code fixes — that one step, and only for SAST.
Automatic remediation for SAST findings
Take findings, generate trustworthy code fixes.
- →Ingests SAST findings from Checkmarx, Fortify, CodeQL, Snyk, SonarQube, Semgrep/Opengrep, Polaris, Datadog — or scans with bundled open-source Opengrep
- →Hybrid deterministic + GenAI fixes, with explanations, PowerUps, and developer-reviewed PRs / bulk commits / .diff
- →Vibe Shield fixes AI-generated code in the IDE (MCP); Tracy tracks AI-authored lines; strong false-positive filtering
- →SAST-only — no SCA, secrets, container, IaC, cloud/runtime, prioritization, SBOM, PR-gating, or workflow/SLO; validation is heuristic + SAST re-scan, not a build
Context-engine native, service-modeled
Prevent, Fix, and Operate on one model.
- →One context engine across six dimensions (code, cloud/runtime, business, ownership, threat, agent), sensor-less and read-only
- →Its own detection — SAST (20 languages), build-emulation SCA (14 ecosystems), secrets — no second scanner required
- →Prioritizes by runtime reachability and internet exposure; guardrails gate new risk; workflows operate to a runtime-verified close
- →Remediation makes the change and proves it: first-party code and dependency edits, built in a sandbox and repaired in your CI until green, opened as a merge-ready PR
Side-by-side, with a verdict per row.
Five states. Heeler-leaning where Heeler advances; explicit when Mobb leads; honest about parity.
Heeler advantage
Heeler delivers a capability Mobb does not, or in a fundamentally different 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.
Mobb edge
Both deliver the capability. Mobb's implementation leads on a verifiable dimension.
Mobb advantage
Explicit signal that Mobb leads on this row.
Scorecard — 24 capabilities, scoped to code security
| Section | ● Heeler advantage | ◐ Heeler edge | ✓ Parity | ◑ Mobb edge | ○ Mobb advantage |
|---|---|---|---|---|---|
| Prevent | 5 | 0 | 0 | 0 | 0 |
| Fix | 3 | 0 | 1 | 0 | 0 |
| Operate | 13 | 2 | 0 | 0 | 0 |
| Total | 21 | 2 | 1 | 0 | 0 |
| Capability | Heeler | Mobb | Verdict |
|---|---|---|---|
| Prevent · stop risk before and as it enters | |||
| Security at AI code generation | Heeler's MCP server + auto-loaded Agent Skills secure code as the agent writes it — across the full surface: SAST weaknesses, secrets, vulnerable and compromised dependencies, and license and minimum-package-age policy — injecting org-specific context to steer secure generation. | Mobb Vibe Shield scans and fixes the agent's generated code in the IDE via MCP — SAST only. | ● Heeler advantage |
| CLI / local developer scanning | The Heeler CLI runs local scans across the full surface — SAST, SCA, and secrets (with live validation) — for shift-left use pre-commit or in any pipeline. | The Mobb CLI ('Bugsy') scans with bundled Opengrep and generates fixes locally or in a pipeline — SAST only. | ● Heeler advantage |
| PR guardrails & policy enforcement | Block / Warn / Observe guardrails gate pull requests on new SAST, SCA, secrets, and SLO violations — plain-English, runtime-scoped, native status checks across GitHub, GitLab, Bitbucket, and Azure DevOps — with an in-PR validated fix. | No documented capability. | ● Heeler advantage |
| Software supply-chain prevention (deps) | Malicious/compromised-package blocking, typosquat detection, minimum-package-age cooldown, unpinned-dependency and unpinned-Action detection, dependency-hygiene scoring, and posture validation across ecosystems — enforced at the PR and continuously re-evaluated. | No documented capability. | ● Heeler advantage |
| IaC pull-request guardrails | Heeler evaluates the Infrastructure-as-Code findings introduced by a pull request instead of failing a change because of pre-existing repository debt. It returns a native source-control check controlled by program policy: Observe records the violations and the decision that would have been made, Warn reports them without preventing merge, and Block fails the check so repository rules can require it. The same policy can move from observation to enforcement without creating separate rules or CI logic. | No documented capability. | ●Heeler 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 on every scan; confidence- and effort-scored, with human review. | A SAST fixer — a hybrid of deterministic rules ('Stable') and GenAI ('Adaptive'), with fix explanations, PowerUps (one fix clearing many findings), and developer-reviewed PRs. Comparable at generating a SAST code fix. | ✓ Parity |
| SCA autofix | Deterministic upgrade selection across 14 ecosystems — the lowest version that clears the CVEs and adds none; for transitive vulns, the smallest first-party ancestor bump that resolves the closure — applied and validated through the CI-repair loop. | No documented capability. | ● Heeler advantage |
| Validated, merge-ready fixes (build + CI repair) | Every fix is built in an isolated sandbox before the PR exists; then Heeler listens for CI results and repairs its own build/test failures with up to five follow-up commits, or hands off. The PR ships with proof it builds — human review, no auto-merge. | Mobb validates a fix with heuristics and by confirming the SAST tool no longer flags it on re-scan — it does not compile the project, run tests, or repair a failing CI pipeline. | ● Heeler advantage |
| Policy-managed remediation memories | Heeler stores administrator-approved remediation guidance as memories in program policy and scopes each memory to an organization or repository. On later remediation runs, the Remediation Agent retrieves the applicable memories and applies the repository’s conventions and constraints when generating a fix. The guidance is centrally governed and reusable; it is not a one-off prompt, a triage-suppression rule, or generic model memory. | No documented capability. | ●Heeler advantage |
| Operate · detect, prioritize, and run the program | |||
| SAST detection | Path-aware, interprocedural source-to-sink taint analysis across functions and files, from Heeler's own engine, with rules adapted per codebase and automatic triage. 20 languages. | Mobb can scan on its own only via bundled Opengrep — commodity open-source SAST, not a first-party engine — and is built to consume a separate scanner's findings. | ◐ Heeler edge |
| SCA detection | Build-emulation SCA across 14 ecosystems (no lockfile, no build required) — direct, transitive, first-party, and bundled dependencies, plus GitHub Actions as a first-class ecosystem. | No documented capability. | ● Heeler advantage |
| Secrets detection & validation | First-class secrets: full git-history scanning with commit attribution, live/active validation via per-provider parsers, scheduled re-validation, rule- and entropy-based, offline in the CLI. | No documented capability. | ● Heeler advantage |
| Agent-file detection & governance | Heeler inspects pull-request changes to agent instructions, skill files, hooks, subagents, and MCP configuration, including CLAUDE.md, AGENTS.md, and SKILL.md. It combines deterministic checks, LLM-based intent analysis, and analysis of external references to identify prompt injection, tool poisoning, unsafe command execution, secret access or exfiltration, permission bypass, and related agent-control risks. The evidence and verdict are returned through Observe, Warn, or Block before the changed artifact is trusted by coding agents. | No documented capability. | ● Heeler advantage |
| Triage & false-positive reduction | Automatic triage folds in reachability and runtime exposure to suppress unreachable and low-impact findings and surface exploitable ones — across SAST, SCA, secrets, and agent-file findings — before you ever see them; false-positive and mitigation verdicts persist across scans. | Deterministic false-positive rules reliably flag non-vulnerable instances across supported scanners — but SAST only, and by pattern rather than runtime context; it cuts SAST triage toil, it doesn't prioritize. | ◐ Heeler edge |
| Runtime-aware prioritization | Heeler Risk (Urgent / Plan / Defer) ranks every finding — SAST, SCA, secrets, and agent-file alike — by whether it's reachable, deployed, and internet-facing, weighted by service tier and threat, with a per-finding SLO. | No documented capability. | ● Heeler advantage |
| Unified context graph | One graph unifying six dimensions — code, cloud/runtime, business, ownership, threat, and agent — sensor-less, with everything downstream (prioritization, SLOs, workflows) reading from it. Cloud/runtime and endpoint discovery are broken out in the rows below. | Not offered — Mobb has no native context engine. It ingests findings from other scanners and builds no unifying graph for downstream prioritization or workflows to read from. | ● Heeler advantage |
| Cloud & runtime context | Fingerprints running services to the exact commit, detects internet-facing exposure, and maps deployments and service-to-service connections across AWS, GCP, Azure, Kubernetes, and Vercel — sensor-less. | Not offered — Mobb has no cloud or runtime model. It does not fingerprint running services, detect internet-facing exposure, or map deployments and service connections. | ● Heeler advantage |
| Endpoint & API discovery | Enumerates application API endpoints and schemas from source, and models per-endpoint authentication — method, protocol, provider, protected-or-not, auth chain, and token issuer. | Not offered — Mobb performs no application API/endpoint discovery and no per-endpoint authentication modeling. | ● Heeler advantage |
| SBOM & dependency inventory | A live dependency inventory and CycloneDX SBOMs at five scopes — a global SBOM across your whole environment, application, repository, and runtime SBOMs per running service and deployment. | No documented capability. | ● Heeler advantage |
| Lifecycle, workflows & SLOs (operate the program) | Findings run Active → Fixed → Deployed with runtime-verified closure, and event-driven workflows route to the owning team (Jira/Linear/Shortcut, Slack/Teams), auto-trigger validated remediation PRs, and close SLOs only on a runtime-verified fix. | No documented capability. | ● Heeler advantage |
| Sensitive-data inventory | Heeler analyzes application source to identify the sensitive data types handled by the code and records them in the application catalog. Each data type is connected to the repositories, services, APIs, and owning teams that process it, together with business criticality and available deployment or exposure context. A reviewer can move from a data type to the applications and interfaces that handle it and evaluate that data-handling risk with the same ownership and runtime context used for other application-security findings. | No documented capability. | ●Heeler advantage |
| Infrastructure-as-Code findings | Heeler evaluates Infrastructure-as-Code against policy and creates a persistent finding for each violation. The finding retains the affected file or infrastructure resource and links it to the repository, application, owning team, business criticality, and any connected cloud or runtime object. The same finding is used for prioritization, lifecycle tracking, workflow routing, pull-request guardrails, and remediation instead of remaining a one-time CI scanner result. | No documented capability. | ●Heeler advantage |
| GitHub audit-log behavioral checks | Heeler ingests the GitHub organization audit log for each connected organization and evaluates defined patterns across the resulting source-control events. A match creates a native finding that retains the actor, action, affected repository or organization, timestamp, and matching event context. The finding enters Heeler’s ownership, prioritization, investigation, workflow, and lifecycle tracking rather than remaining a searchable or exported log event. | No documented capability. | ●Heeler advantage |
| OpenSSF Scorecard repository health | Heeler records the repository’s OpenSSF Scorecard result and each check outcome on the repository object in the catalog. The same repository view links the signal to applications, ownership, vulnerabilities, dependency health, provenance, deployment, and exposure context. Teams can inspect which Scorecard checks reduced the result and evaluate repository-maintenance risk in relation to the applications that use the repository instead of treating the score as an external badge. | No documented capability. | ●Heeler advantage |
Where an end-to-end platform beats a fix layer.
Every one of these maps back to the context engine — not features bolted onto a scanner.
One platform, at machine speed for the AI SDLC
AI writes code faster than any review process can keep up. Heeler runs the whole loop — prevent, detect, prioritize, fix-and-prove, operate to verified closure — continuously and at machine speed on one context engine, so security keeps pace with the volume of AI-generated code. Mobb does one step of that loop, SAST fixing, on findings another tool produced.
One product, not two
Heeler detects and fixes on one platform, from its own SAST, SCA, and secrets engines. Mobb has no first-party detection of its own — its bundled scanner is open-source Opengrep — so in practice you run a separate commercial scanner to feed it. That's two products, two bills, and still SAST-only coverage.
The whole surface, not just SAST
Heeler prevents, detects, and fixes across SAST, SCA, secrets, licenses, minimum-package-age, and compromised dependencies, plus CI/CD supply chain and agent files — all unified with runtime and cloud in one context engine. Mobb is SAST-only; everything else is out of scope.
Fixes proven in CI, not just re-scanned
Heeler builds every fix in an isolated sandbox and repairs its own CI failures until green — for code and dependencies. Mobb validates by heuristics and a SAST re-scan; it never compiles or tests the change, so a fix that re-scans clean can still break your build.
Prioritize before you fix — across every finding type
Heeler ranks SAST, SCA, secrets, and agent-file findings by what's actually reachable, deployed, and internet-facing, so effort lands where it matters. Mobb filters false positives, but fixes what it's handed without runtime-aware prioritization.
See Heeler across your AI SDLC.
Heeler secures the whole AI SDLC — not just code. A demo runs it against your real repos and cloud: detection, the runtime map, prioritization, validated remediation PRs, and the agent-file catalog. If Mobb is remediating one scanner's SAST backlog today, we can show the rest of the loop around it.
