June 4, 2026
100 enterprise AI agents rated, plus an open source methodology you can use in procurement and security posture tracking.
Read more: AIRQ: AI Risk Quadrant — independent AI agent security rating (opens in new tab)June 4, 2026
100 enterprise AI agents rated, plus an open source methodology you can use in procurement and security posture tracking.
Read more: AIRQ: AI Risk Quadrant — independent AI agent security rating (opens in new tab)June 4, 2026
100 enterprise AI agents rated, plus an open source methodology you can use in procurement and security posture tracking.
Read more: AIRQ: AI Risk Quadrant — independent AI agent security rating (opens in new tab)
[ Coding Agent Security Platform ]
Securing the AI agents writing code, and the code itself
Agentic development security (ADS) protects AI-powered software development end to end: prevention, detection, prioritization, and remediation across code, dependencies, workflows, and running applications. Adversa AI covers the agent side of ADS: runtime visibility and enforcement over the coding agents, MCP servers, skills, and toolchains that now do the development.
Application security was built for human-paced development, and coding agents changed the pace.
Coding agents ship code that works and is exploitable by default: unauthenticated endpoints, broken authorization, missing input validation. They ship it faster than any review queue can absorb.
When agents write the code, security has to decide by policy as the work happens. A queue of findings waiting for a human is the bottleneck, not the control.
AppSec was built around scans at fixed points in the SDLC. Agents work continuously across the IDE, the CLI, and CI, and much of what they do never reaches a scan.
ADS must address two entirely different engineering problems, solved by different vendors.
Analysis, triage, and remediation: where AST is heading
The code and dependencies agents produce, analyzed for exploitability, ranked, and fixed. This is the half the AppSec market is racing toward, and the one your AST and ASPM vendors are already extending.
Guardrails, toolchain protection, and policy gates at runtime
The harness, MCP servers, skills, and extensions, and what agents do with a developer’s credentials: shell commands, file writes, pushes, outbound calls. This half has no AST heritage. It is where Adversa AI works.
Why the second half needs its own controls: a poisoned repo or skill gets installed. The agent ingests it and starts acting on an attacker’s instructions. It reads a secret. It pushes a commit — your API keys inside. Every filter you run said yes four times, and the code itself may be clean. No single event was suspicious, but the chain of events became the breach.
The platform builds an agent decision graph: every action, its inputs and its effects. Then it scores risk across the chain as it forms, across the model, the tools, and the machine. When the chain crosses the line, it’s blocked before it completes.
Attacks are less frequent than mundane mistakes and agentic misbehavior. The same chain analysis catches all three sources of damage.
[ 01 ]
“What else is the agent doing?”
Agents gradually expanding their own scope, acting far beyond the original task, runaway loops burning compute and tokens.
[ 02 ]
“Can one wrong command drop production?”
A “test cleanup” that drops the production database; a quick commit carrying your secrets into public code; a key pasted into a prompt.
[ 03 ]
“Can someone hijack your agent?”
Prompt injection and malicious context: a poisoned repo or skill, a rogue MCP server, and the exfiltration chain each one starts.
There are eight distinct ADS capabilities. Five of them concern the work of AI agents, and the rest focus on the software code they produce. Here are the pillars we cover:
| What ADS asks for | Coverage | Where Adversa AI fits |
|---|---|---|
| Guardrails for AI-assisted coding that steer agents to safe outcomes and stop unsafe instructions from running | Covered | The core of the platform. 100+ attack-anchored policies stop unsafe commands, injected instructions, and dangerous action chains before they complete. |
| Supply chain and toolchain protection, covering coding agents, extensions, MCP servers, agent skills, pipelines, and artifacts | Covered | Agents, MCP servers, skills, and extensions controlled at runtime: what each one may touch, and what it actually did. Package and artifact scanning stays with your SCA. |
| Dynamic testing of live applications and APIs, including flaws from the OWASP Top 10 for LLM Applications | Covered | Through our AI red teaming services, not the platform: the offense team that co-authors the OWASP LLM and Agentic Top 10 tests your LLM applications and agents. |
| Triage and prioritization that ranks findings by exposure and business impact | Partial | For agent activity: every detection arrives scored as a chain, with its evidence, and routed to your SIEM by severity. Code findings stay with your AST or ASPM. |
| Governance, reporting, and risk analytics that hold over time | Partial | The agent record: every action logged and export ready for audit, with coverage reporting across your approved agents. Code risk trends stay in your ASPM. |
Other ADS capabilities belong to your AST and ASPM vendors. Secure agentic development needs both halves; we make the agent half enforceable.
[ Platform features ]
Every action of every approved agent, across every brand: the systems touched, the commands run, the data moved, including cooperating agents and subagents no single-agent view can show. The record that governance and reporting build on.
Policies decide inline, as the agent works: warn, deny, or require a human in the loop, per policy, per team, per environment. No alert queue between the decision and the action.
You define what’s allowed and forbidden in a sentence; the platform turns it into an enforceable policy. 100+ attack-anchored defaults ship out of the box, distilled from our red team’s confirmed findings.
Prompt injection and malicious context caught where it hides: untrusted repos and skills, readmes, MCP servers, tickets. Content is one signal among trust state, action type, identity, and provenance, so a reworded attack trips the same rule.
MCP servers, skills, and extensions watched as the agent uses them: what each one is allowed to touch, and what it actually did. Sensitive data and technical secrets flagged before they leave.
Violations triaged and routed to your SIEM and alert queues with severity thresholds. Every alert arrives with the chain, the rule, and the evidence.
Two components with a single dashboard and centralized policy engine.
Intercepts the agents’ own traffic: LLM calls, responses, tool executions, and MCP calls. Inline, or reading from the LLM/API gateway you already run.
Watches actions on the machine: files, shell, CI/CD. Strictly the agent’s actions, not the developer’s.
Every action is logged into the agent decision graph and tied to the run that produced it: which agent, doing what, triggered from where. Each action is scored and gets its own verdict, the chain is scored as a whole. The response follows your policies: warn, deny, or human-in-the-loop.
[ Policies ]
100+
Attack-anchored policies out of the box
The policies are adapted to real developer workflows, past incidents and attacks that actually work. Each one is distilled from our red team’s confirmed offense findings.
Write the sentence in plain English: “no agent in the dev team ever writes to production servers.” The platform turns it into an enforceable policy. It can turn a screenshot of an AI incident into one, too.
Rules match the shape of dangerous behavior. A reworded attack trips the same rule.
[ Trust & Proof ]
Adversa AI experts are co-leads and core members of industry-defining frameworks and initiatives: NIST AI RMF, OWASP ASI, CoSAI, CSA AI CM. Trusted by Fortune 500 enterprises including top banks, insurance companies, fintech, Big Four, and automotive enterprises.
Analyst recognition
Standards leadership
A decade of adversarial research
20+ industry awards
20+ industry awards
[ See it yourself ]
Start with a scoped, observe-mode pilot: one dev team, the agents you’ve approved. See the would-have-blocked list on your own traffic, and turn on blocking when you’re ready.
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Agentic development security (ADS) protects AI-powered software development end to end: prevention, detection, prioritization, and remediation across code, dependencies, workflows, and running applications, with security decisions made autonomously by policy rather than raised as alerts. In practice it covers two halves, the code agents write and the agents writing it.
Forrester introduced the term in April 2026, in a post arguing that application security needs a new operating model for AI-powered development. Forrester has since announced a landscape report and a Forrester Wave evaluation of the category. The two-halves reading and the capability map on this page are ours, not Forrester’s.
Application security testing finds flaws in code at fixed stages of the SDLC and hands them to people to triage. ADS assumes agents write much of the code and act inside the toolchain, so analysis, triage, remediation, and gating have to run continuously and decide by policy. It also covers what AST never did: the agents, extensions, MCP servers, and skills themselves.
Coding agent security is the agent half of ADS: constraining what an AI coding agent can execute, what it can reach, and which instructions it accepts from content it did not write. ADS is the wider frame, which also covers analyzing and fixing the code agents produce.
No. We cover the agent half: guardrails for coding agents, toolchain protection for MCP servers, skills, and extensions, policy gates on what agents do, and the audit record that governance needs. Code and dependency analysis and automated remediation belong to your AST or ASPM vendor, and we complement them. Dynamic testing for LLM application flaws comes from our AI red teaming services, not the platform.
AI detection and response (AIDR) is runtime security for AI: it watches what agents do and enforces policy at the point of execution. On the agent half of ADS, AIDR for coding agents is how guardrails, toolchain protection, and policy gates actually run. ADS is the program; AIDR is one of the controls inside it.
The agents you’ve approved — Claude Code, Copilot, Cursor, Codex, and what comes next. Policies hold when developers switch tools.
14–30 days, one dev team, observe mode only, success criteria agreed in writing before the start — including measured overhead on your own traffic and the would-have-blocked list that justifies turning blocking on.
