5 Best ADLC Security Tools for Teams Shipping with Coding Agents (2027)

Coding agents now write a large share of production code. They also install packages, run shell commands, and call MCP servers on their own, at machine speed. The best ADLC security tools keep up with that work: they act inside the agent loop as well as at the pull request, and they return findings with enough evidence that developers trust them.

Below are the five top ADLC security tools for 2027, starting with Endor Labs, a Leader in The Forrester Wave™: Agentic Development Security Tools, Q4 2026, followed by what each one does and which teams it fits.

Best ADLC security tools at a glance

1. Endor Labs: A Leader in The Forrester Wave™: Agentic Development Security Tools, Q4 2026, and the top pick for securing the full agentic development lifecycle, from coding agent governance and package installs to AI-generated code review and release.
2. Checkmarx: Developer Assist checks code as it's written by developers and AI coding agents.
3. Socket: Install-time blocking of malicious open source packages.
4. Semgrep: Real-time static analysis feedback for AI copilots through the Semgrep MCP server.
5. GitHub Advanced Security: CodeQL code scanning with Copilot Autofix for repositories on GitHub.

What are ADLC security tools?

ADLC security tools control what AI coding agents can access, do, install, and ship, from the moment code is generated through release. Forrester calls this category agentic development security tools. They cover author time, while the agent works, and build time, when the agent's output moves through PR, CI, and deployment.

The agentic development lifecycle (ADLC) differs from the SDLC because agents act on their own. They pick dependencies, write files, run commands, and connect to tools between one prompt and the next. A scanner that only runs when a PR opens sees the finished change, not the package install or the MCP call that happened along the way. In Endor Labs' Agent Security League, the top coding agent as of October 2026 wrote patches that passed functional tests 87.2% of the time, but only 37.4% also passed security tests.

For a walkthrough of each stage, see the secure agentic development lifecycle explained.

How we chose these tools

We ranked each tool on five things that matter most when agents write the code:

• Where it acts. Controls that run inside the agent loop, through hooks, MCP, or a CLI, catch problems before they reach a PR. Controls at PR, CI, and release catch what slips through.
• Coverage across the lifecycle. Agent governance, dependencies, first-party code, secrets, containers, and the AI components inside applications each need coverage.
• Evidence behind findings. Reachability, dataflow, and auditable AI triage separate real risk from noise.
• Fit with agent workflows. Support for the coding agents and IDEs developers already use, such as Claude Code, Cursor, and Codex.
• Pricing that scales with agent use. Token-based billing for security agents climbs as agents write more code. Predictable pricing lets teams expand coverage without watching a meter.

1. Endor Labs

Endor Labs is an AI-native application security platform built to secure software from the moment a coding agent starts working through the moment a release ships. It combines coding agent governance, install-time package protection, AI-driven code review, reachability-based SCA, secrets detection, container security, and artifact signing in one platform with one policy engine.

Forrester named Endor Labs a Leader in The Forrester Wave™: Agentic Development Security Tools, Q4 2026, and gave it the highest score possible in seven criteria: AI SCA, AI application flaws, AI triage, AI prioritization, AI coding toolchain, innovation, and pricing flexibility and transparency. Download a complimentary copy of the report.

Key ADLC security features:

• AURI by Endor Labs. AURI is a security harness for agentic software development. It brings Endor Labs analysis into the agent loop through Hooks, Skills, MCP, CLI, and API, so coding agents like Claude Code, Codex, Cursor, and Devin get findings and fixes while they write code, before a PR exists. Across 34 security prompts, the same model used 12x fewer tokens with Endor Labs evidence than without it.
• Coding Agent Governance. An inventory of every coding agent, model, MCP server, and skill in use across developer environments, with policy enforced from a unified control plane. Native hooks evaluate risky actions, including MCP tool calls, shell commands, and reads of sensitive files, and can block them regardless of what the model decides. Deployment is agentless and can go live through Cursor Enterprise, Claude Enterprise, or MDM.
• MCP server and skill review. Coding Agent Governance assesses MCP servers and skills for risk, including the OWASP Top 10 for LLMs, and supports allow and deny lists so only reviewed components reach developers.
• Package Firewall. Evaluates install requests from agents and developers before packages land. It blocks malicious packages using a real-time malware feed, enforces cooldown windows and license policies, and logs the package version, agent, and developer for each decision. That matters because models often suggest packages that don't exist, and attackers register those names. Research presented at USENIX Security 2025 found hallucinated package names across all 16 code-generating models it tested.
• AI SAST. Orchestrates detection, triage, and remediation agents that combine rule-based pattern matching, dataflow tracing, and multi-pass LLM reasoning. It catches the business logic, broken access control, IDOR, and insecure design flaws that are common in AI-generated code and hard to express as rules. Triage labels each finding as true positive, false positive, or unknown, so reviewers can see the reasoning. Across 8 real-world projects in Java, Python, JavaScript, TypeScript, C#, and Go, with ground truth set by the Endor Labs security research team, AI SAST found 192 real vulnerabilities, 2.6x what Claude Opus 4.7 found and 3.5x what Codex (GPT-5.5) found, including 63 that no other tool in the test caught. See the multi-agent approach to detecting security design flaws for how it works.
• AI Security Review. Runs AI SAST on every pull request in GitHub, GitLab, and Bitbucket. One scan covers changed code, changed dependencies, and introduced secrets, and posts a context-aware fix with each confirmed vulnerability. According to Endor Labs, fewer than 10% of PRs need human security review after rollout.
• Endor Open Source with full-stack reachability. Traces reachability across first-party code, dependencies, and container images to show whether a vulnerable function is actually called, with an average 92% reduction in SCA false positives, according to Endor Labs. Upgrade Impact Analysis ranks upgrade paths by breaking-change risk, and Endor Patches backports fixes when an upgrade would break your code.
• Secrets Detection. Scans full Git history, validates whether found credentials are live or revoked, and stops leaks at pre-commit, where agents writing config files and tests at speed are most likely to introduce them.
• Container Scanning and Artifact Signing. Container Scanning extends reachability through OS and runtime layers, with up to 90% container noise reduction according to Endor Labs. Artifact Signing signs images, binaries, and build artifacts and verifies them at deployment.
• AI Application Security. Detects AI models and components shipping inside applications and catches OWASP LLM Top 10 risks, such as prompt injection, sensitive information disclosure, and improper output handling, before production. It gives regulated teams the inventory that frameworks such as the EU AI Act and ISO/IEC 42001 call for.
• Per-developer pricing. Endor Labs is priced per contributing developer, and new AI capabilities are included without separate token-based billing.

Best for: AppSec and platform engineering teams that want one platform to govern coding agents and secure everything those agents write, install, and ship, without stitching together point tools.

2. Checkmarx

Checkmarx offers the Checkmarx One application security platform. Its Developer Assist product checks code for security issues as developers and AI coding agents write it, guides fixes, and rescans changed code to confirm the issue is gone. It covers application code, vulnerable and malicious open source packages, exposed secrets, IaC misconfigurations, and container risks, and its remediation features are available through Checkmarx MCP.

Best for: Enterprises already running Checkmarx One that want in-IDE checks for AI-assisted coding.

3. Socket

Socket is a software supply chain security company focused on open source packages. Socket Firewall works at the network level: it blocks downloads of disallowed package versions and removes them from the registry metadata that agents and package managers see. Socket also powers the package firewall in Replit, which blocks malicious, typosquatted, and slopsquatted packages across npm, yarn, pnpm, pip, and Go modules.

Best for: Teams whose first priority is stopping malicious packages at install time.

4. Semgrep

Semgrep is a static analysis platform with customizable rules. The Semgrep MCP server brings Semgrep scanning into the loop with developers and their AI copilots, giving real-time feedback while code is being written.

Best for: Teams that write their own static analysis rules and want them enforced in AI-assisted workflows.

5. GitHub Advanced Security

GitHub Advanced Security adds code scanning powered by CodeQL, secret scanning, and dependency review to GitHub repositories. Copilot Autofix identifies vulnerabilities, explains them, and suggests code fixes, including for existing alerts.

Best for: Organizations whose code lives entirely on GitHub and that want built-in scanning on pull requests.

ADLC security tools compared

How Endor Labs secures one agent task, end to end

Following a single task shows why coverage across the lifecycle matters. A developer asks a coding agent to add an order-history endpoint. While working, the agent:

1. Calls an MCP server connected to the production database, with write permissions it doesn't need.
2. Installs an unfamiliar date-formatting package published days ago by an unknown author.
3. Writes an endpoint that fetches orders by ID without checking that the order belongs to the logged-in user, an IDOR flaw.
4. Hardcodes an API key from .env into a test file.
5. Opens a PR.

With Endor Labs in place:

• Step 1: Coding Agent Governance hooks evaluate the MCP tool call before it runs and block or route the write for approval.
• Step 2: Package Firewall stops the install before the package's install script can execute.
• Step 3: AURI runs AI SAST in the agent loop, flags the missing ownership check, and the agent applies the fix before the PR opens. If anything gets through, AI Security Review catches it on the PR with a fix attached.
• Step 4: Secrets Detection confirms the key is live and stops the commit.
• Step 5: The merged change builds into a signed artifact verified at deployment, and Container Scanning reports only the vulnerabilities the application actually exercises.

Most AppSec stacks see agent work only at the pull request, by which point steps 1 and 2 have already happened. Our ADLC security best practices guide covers the controls behind each step.

How to choose an ADLC security tool

• Start with where your agents act. If agents install packages and call MCP servers, you need controls inside the agent loop, not only at PR.
• Count the tools you'd need. Point tools can each cover one stage, but separate policies and inventories leave gaps between them. A platform that covers governance, dependencies, code, secrets, and release in one place keeps policy consistent.
• Ask for evidence. Look for reachability and dataflow proof, and AI triage you can audit.
• Check agent support. Confirm the tool works with the agents and IDEs your developers use today.

For a broader platform comparison beyond agentic workflows, see application security tools compared for the AI era.

FAQs

What is the best ADLC security tool?

Endor Labs is the top ADLC security tool for teams shipping with coding agents because it covers the full lifecycle in one platform: coding agent governance, install-time package protection, AI SAST in the agent loop and on every PR, reachability-based SCA, secrets, containers, and artifact signing.

Can existing SAST and SCA tools secure AI-generated code?

Existing SAST and SCA tools cover part of the problem, mainly vulnerable dependencies and code-level flaws found at PR and in CI. AI-generated code also needs checks inside the agent loop, install-time package protection, and review that reasons about logic flaws such as IDOR and broken authorization.

Do ADLC security controls slow down coding agents?

Well-designed ADLC security controls speed agents up, because they catch problems while the agent still has context and can fix them in the same session. Hooks block only the riskiest actions, and evidence-backed findings keep developers from chasing noise.

Secure your coding agents with Endor Labs

AURI by Endor Labs works with Claude Code, Cursor, and other coding agents your developers already use. Try it on a real repo and a real agent task, or read the AI Security Review documentation to get set up.

Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. This report is part of a broader collection of Forrester resources, including interactive models, frameworks, tools, data, and access to analyst guidance. For more information, read about Forrester’s objectivity here.