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Heeler vs Backline.
Agentic Development Security, built for the AI SDLC — versus Backline, an autonomous remediation layer for findings other tools produce. Heeler detects the problem and computes the fix deterministically, then puts an agent to work building and proving that fix in your CI; Backline performs no detection of its own and runs agents over the findings your existing scanners hand it. Here's where each approach leads.
Heeler finds it and fixes it; Backline fixes what your other tools found.
Heeler runs its own scanners for code, dependencies, secrets, Infrastructure-as-Code, and CI/CD on one context engine that unifies code, dependencies, runtime, and cloud. Detection is deterministic — the same commit returns the same findings — and so is the fix: a multi-signal solver selects the upgrade that clears the most risk with the least disruption, and SAST fixes use strategy-matched transforms anchored to the exact source-to-sink flow. The agent's job begins only after the fix is decided, building it in an isolated sandbox and repairing its own CI failures until the pull request is green. Determinism where the answer has to be repeatable; AI where iteration is the job.
Backline is an autonomous remediation layer that performs no scanning of its own. It connects to a customer's existing scanners, consolidates and deduplicates their findings, and uses four agents — ANDY, POLLY, CODIE, and VERA — to analyze, plan, code, and validate fixes delivered as ready-to-review pull requests. Its coverage begins where another vendor's detection ends.
Heeler detects and decides the fix deterministically, then lets an agent prove it. Backline runs agents over findings other vendors produced.
Both generate fixes and validate them before a human reviews. Two things set Heeler apart. It produces the findings in the first place — code, dependencies, secrets, Infrastructure-as-Code, and CI/CD — on a context engine that models where that code runs, what's exposed, and who owns it. And it decides the fix deterministically before any model runs: the upgrade is chosen by a multi-signal solver, and SAST fixes by strategy-matched transforms anchored to the exact source-to-sink flow, so the same finding returns the same fix strategy every time. The agent is then pointed at what it is genuinely better at — building the change, reading the failure, and repairing it until CI passes. Backline's pipeline is agent-driven from analysis through code generation, over a backlog someone else detected.
Remediation point solution
Ingest other tools' findings; fix them.
- →No scanners of its own — ingests findings from Tenable, Qualys, Wiz, Orca, Snyk, Mend, Semgrep, Trivy, Prisma, CrowdStrike, and AWS Security Hub, among others
- →Four agents — ANDY, POLLY, CODIE, and VERA — analyze, plan, code, and validate the fix, delivered as a ready-to-review pull request
- →Consolidates and deduplicates those findings into one view, ranked by severity, SLA, exploitability, reachability, and asset importance using the ingested tools' own signals
- →Documented remediation coverage spans dependency (SCA), container image CVEs, Infrastructure-as-Code and cloud misconfigurations, exposed secrets, and identity risks; no detection of its own, no pre-merge gate, no SLO model, no MCP server, no agent-file governance
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
Side-by-side, with a verdict per row.
Five states. Heeler-leaning where Heeler advances; explicit when Backline leads; honest about parity.
Heeler advantage
Heeler delivers a capability Backline 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.
Backline edge
Both deliver the capability. Backline's implementation leads on a verifiable dimension.
Backline advantage
Explicit signal that Backline leads on this row.
| Section | ● Heeler advantage | ◐ Heeler edge | ✓ Parity | ◑ Backline edge | ○ Backline advantage |
|---|---|---|---|---|---|
| Prevent | 4 | – | – | – | – |
| Fix | 2 | 1 | 1 | – | – |
| Operate | 11 | 6 | – | – | – |
| Total | 17 | 7 | 1 | – | – |
| Capability | Heeler | Backline | 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. | No documented capability. | ●Heeler advantage |
| CLI / local developer scanning | The heelercli runs local scans across the full surface — SAST, SCA, secrets (with live validation), license, and malicious-package checks — with non-zero exit for pre-commit and CI, SARIF output, and policy-as-code; plus IDE reach through the MCP server. | No documented capability. | ●Heeler advantage |
| 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, SAST, secrets, and SLO breaches — scoped by runtime context (enforce only on Tier-1, production, or internet-facing services) — with a validated fix committed straight into the PR branch. Block relies on SCM branch protection to enforce. | 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 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. | No documented capability. | ●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#. | Backline performs a deep-context analysis of the dependency graph to identify the most stable upgrade path that eliminates the vulnerability without introducing breaking changes, and flags cases where no safe upgrade path exists for an engineer to handle. | ✓Parity |
| 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. | Backline runs a validation layer before proposing a fix, executing the customer's existing unit tests, performing dependency checks, and confirming no regressions are introduced, and its Verify stage can iterate when CI fails. What is not documented is building the change first in an isolated sandbox with the project's own toolchain, then repairing its own build and test failures on the open pull request until CI passes. | ◐Heeler edge |
| 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 — 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. | Backline performs no static analysis of its own. | ●Heeler advantage |
| Dependency vulnerability (CVE) detection | Build-graph SCA across 14 ecosystems that resolves the full direct-and-transitive graph itself, from your manifests and lockfiles — using a committed lockfile when present, without requiring one, and with no build change — covering first-party and bundled dependencies too, matched with CVSS, EPSS, and known-exploited signals. GitHub Actions and reusable workflows are a first-class dependency ecosystem, scanned like any other. | Backline performs no dependency scanning of its own. It connects to existing scanners such as Snyk, Mend, and Trivy and ingests their raw vulnerability data for consolidation and remediation. | ●Heeler advantage |
| Supply-chain & package-reputation detection | Heeler flags 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, provenance, and integrity checks (impostor commits, typosquats, abandoned sources). | No documented capability. | ●Heeler advantage |
| 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. | Backline's agents check whether the vulnerable code path is actually reachable and weigh that alongside severity, SLA, exploitability, and asset importance. Reachability established by two independent signals — the library loaded in a running deployment, plus a static call-graph path to the vulnerable function across six ecosystems — is not documented. | ◐Heeler edge |
| 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; an active secret is weighted as maximal impact), offline structural validation, and 700+ rules across 20+ languages; contained and gated at the PR. | Backline performs no secrets detection of its own; exposed secrets are named as a remediation target once another tool has found them. Git-history scanning, live credential validation, or detection rules of its own are not documented. | ●Heeler advantage |
| Native scanners & detection | Heeler operates its own purpose-built scanners — interprocedural taint, structural, and heuristic analysis for code, plus SCA, secrets, Infrastructure-as-Code, and CI/CD. Detection, triage, prioritization, and remediation share one model and improve together, and there is no dependency on which third-party tools a customer happens to own. | Backline performs no scanning of its own. It connects directly to existing production scanners — including Tenable, Qualys, Wiz, Orca, Snyk, Mend, Semgrep, Trivy, Prisma, CrowdStrike, and AWS Security Hub — and ingests their raw vulnerability data into a central findings store. Coverage, detection quality, and finding accuracy are bounded by the tools a customer already owns, licenses, and operates. | ●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. | No documented capability. | ●Heeler advantage |
| 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. | Backline contextualizes severity and impact with environment tier, service exposure, dependency centrality, package health, and runtime evidence, and its ANDY agent gathers context from the customer's environment. A runtime model Backline builds and maintains itself — deployments fingerprinted and mapped to the running changeset, with service tier and internet-accessibility derived from that model — is not documented. | ◐Heeler edge |
| Cloud, runtime & business context | Heeler builds its own model of where code runs — across AWS, GCP, Azure, Kubernetes, and Vercel, with 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 the owning team (Tech Lead / Security Lead) — all read-only and sensor-less. This context is what makes exposure, prioritization, and routing real. | Backline consolidates findings from cloud and infrastructure tools such as Wiz, Orca, Prisma, and AWS Security Hub, and prioritizes using ownership confidence and whether an asset's exposure is external or internal. A deployment-topology model of its own — service-to-service edges, and deployments mapped to the exact running changeset — is not documented. | ◐Heeler edge |
| 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 |
| 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. | Backline performs a deep-context analysis of the dependency graph when planning an upgrade path, and its agents regenerate SBOMs as part of a remediation. A standing dependency inventory, and SBOM export in a format such as CycloneDX or SPDX, are not documented. | ◐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-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, with a plain-English builder. | Backline integrates with Jira, ServiceNow, Monday, and Slack, consolidates findings across scanners into a single deduplicated view, and provides APIs, flexible dashboards, and reporting tools. | ◐Heeler edge |
| SLOs & runtime-verified closure | Every tracked finding under an SLO policy carries a per-band (Urgent / Plan / Defer) 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. | Backline ranks and plans remediation according to SLA and reports on reduced mean time to remediation. Service-level objectives managed as program policy, and closure confirmed against running deployments rather than at merge, are not documented. | ◐Heeler edge |
| 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. | Backline remediates Infrastructure-as-Code misconfigurations and cloud-native manifests, including fixes such as tightening overly permissive inbound network rules. The underlying misconfiguration findings are ingested from connected scanners; native Infrastructure-as-Code detection with its own policy and finding lifecycle is not documented. | ●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 |
| 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 |
Where an engine that finds and fixes beats a layer that only fixes.
Each maps back to the one context engine — code to cloud — not to signals inherited from someone else's scanner.
Runtime and cloud context that's native, not inherited from the tools you connect
Backline prioritizes using ownership confidence and whether an asset's exposure is external or internal, drawing its cloud picture from connected tools such as Wiz, Orca, Prisma, and AWS Security Hub. What it does not document is a deployment-topology model of its own. Heeler builds a sensor-less model of where code runs — deployment-to-changeset mapping, service-to-service edges, service tier, ownership, and internet-accessibility — so exposure, prioritization, and routing work without a second vendor in the chain.
Prioritize by exposure Heeler computes, not signals inherited from other scanners
Backline ranks findings by severity, SLA, exploitability, reachability, and asset importance, and its agents check reachability and exposure themselves. Heeler gates every dependency finding on reachable AND internet-exposed AND not-mitigated — with reachability confirmed twice over, by a running deployment loading the library and by a static call-graph path to the vulnerable function — and traces SAST exposure from a real internet-facing entry point, so a live, exposed finding outranks a dormant one that looks identical on severity.
The fix is decided deterministically, then proven by an agent in your CI
Backline runs a validation layer before proposing a fix — the customer's existing unit tests, dependency checks, and a no-regressions check — and its Verify stage can iterate when CI fails. Its analysis, planning, and code generation are all agent-driven. Heeler settles the fix before any model runs: a multi-signal solver picks the version that clears the most risk with the least disruption, and SAST fixes use strategy-matched transforms anchored to the exact source-to-sink flow. Only then does the agent build it in a sandbox, run your CI, and repair its own failures until the pull request is green. Deterministic where the answer should be the same every time; agentic where iteration is the whole point.
Operate to a runtime-verified close, with risk-band SLOs
Backline plans remediation against SLA and reports reduced mean time to remediation. Heeler runs per-band SLO clocks and moves findings Active to Fixed to Deployed, closing only when the running deployment confirms the vulnerability is actually gone — “no longer in the branch” is not “no longer running anywhere.”
Beyond detection: the agent, the attack surface, and the agent files
Heeler hands the coding agent live, runtime-aware context through an MCP server as it writes, discovers your full endpoint and API attack surface from source (REST/GraphQL/gRPC/SOAP/WebSocket/MCP, with auth posture and internet-accessibility), and governs the agent-instruction files that now steer your code — scoring CLAUDE.md, .mcp.json, and skills 0–100 for malicious intent. Backline documents four remediation agents that analyze, plan, code, and verify fixes, but no context server for coding agents, no endpoint or attack-surface discovery, and no agent-file governance.
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 Backline is remediating another vendor's findings today, we'll show what owning detection and remediation together changes.
