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 ]
For AppSec and product security teams
Our AIDR platform catches the dangerous agent action chains before they complete. You can’t solve coding agent security with SAST or SCA: they cover the code, but another solution must observe the actions that write this code.
[ Why AppSec teams care ]
Claude Code, Copilot, Cursor: coding agents are now the most active contributors inside your SDLC — and the least accountable. Secrets incidents, review turnaround, policy coverage: every number you report now runs through agents your stack can’t see. If an incident reviewer, an auditor, or your own CISO asked right now:
Which commits were shaped by untrusted context, and which agents could be acting on an attacker’s instructions right now?
Which agent action chains bypassed your production controls?
Which secrets did an agent read this week — and where did they go?
You can’t be accountable for what you can’t observe. Agent observability is the missing layer: every action, connected into chains, across every coding tool you approved.
[ Adversa AI decision graph ]
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: DLP saw a normal commit, EDR saw a developer’s process, the gateway saw a routine completion call. 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.
[ What we stop ]
Hijacking is only one of three ways agents cause damage. The same chain analysis catches the everyday risk too.
[ 01 ]
“What else is the agent doing?”
What we stop: 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?”
What we stop: A “test cleanup” that drops the production database; a quick commit carrying your secrets into public code.
[ 03 ]
“Can someone hijack your agent?”
What we stop: Prompt injection and malicious context: a poisoned repo or skill, a rogue MCP server, and the exfiltration chain each one starts.
[ Beyond the gateway ]
An AI gateway is for traffic control, not risk management. Your LLM gateway or a DIY API proxy is the right infrastructure, but it needs additional capabilities. We enrich it with advanced runtime security. What a gateway can’t do by design is understand behavior:
| Capability | A typical AI gateway | Coding Agent Security Platform |
|---|---|---|
| Routing LLM traffic for models and agents | Yes — keep it | Reads selectively from your gateway, or runs as a standalone gateway |
| Monitoring tool execution and MCP calls, separated from model traffic | LLM traffic only | Yes, logging which systems are touched, which commands run |
| Recognition of what’s actually happening (semantic analysis of the traffic) | No | Yes |
| Combining steps into chains, risk scored progressively as the chain builds | No — each step analyzed separately | Yes — agent decision risk scoring |
| Endpoint agent runtime monitoring (files, shell, CI/CD) | No | Yes |
| Baseline policies: dangerous events and attacks caught by default | Your team must write and maintain rules | 100+ attack-anchored policies out of the box, updated by our offense team |
| LLM guardrails (prompt injection and malicious context) | Basic filters | Comprehensive agent behavior policies, research-driven and based on real-world and PoC attacks |
| Enforcement | Prompt-level filters | Warn, deny, or human-in-the-loop at action level |
The hidden cost of homegrown solutions is maintenance. Attack techniques against coding agents evolve monthly, and somebody has to keep detection current. Adversa AI has dedicated offense and research teams that do it for a living, so it doesn’t become your platform team’s backlog.
[ Platform features ]
Every action of every approved agent, across every brand: the systems touched, the commands run, the data moved. Multi-agent patterns: risky behavior detected across cooperating agents and subagents, which no single-agent view can show.
You define what’s allowed and forbidden, in plain English. The platform turns the sentence into an enforceable policy. 100+ attack-anchored defaults ship out of the box, distilled from our red team’s confirmed findings.
Behavioral detection of the shape of what the agent is doing. Prompt injection and malicious context caught where it hides: untrusted repos and skills, readmes, MCP servers, tickets. Sensitive data and technical secrets flagged before they leave.
Warn, deny, or require human-in-the-loop — per policy, per team, per environment.
Maintained by our red team, refreshed as attacks evolve.
Violations triaged and routed to your SIEM and alert queues with severity thresholds.
[ Platform architecture ]
Two components, one platform:
Intercepts the agents’ own traffic: LLM calls, responses, tool executions, and MCP calls. The component does that inline, or by 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.
Both feed one policy engine. 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 is triggered by one flagrantly dangerous action, or a combination of harmless-looking ones that crosses the risk threshold. It follows your policies: warn, deny, or human-in-the-loop.
What we break in the lab, the runtime blocks by default.
[ 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. Get a first signal in minutes. Turn on blocking when you’re ready.
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The agents you’ve approved — Claude Code, Copilot, Cursor, Codex, and what comes next. Policies hold when developers switch tools.
The platform covers your sanctioned fleet: the agents you’ve approved and routed. It won’t hunt unknown tools across the estate, but coverage reporting shows which approved agents are enrolled — and where the gaps are.
No. Your gateway keeps routing; we read from it selectively and add the layers it can’t see: tool executions, endpoint actions, and chain-level analysis with enforcement. No gateway yet? The agent security gateway runs as one.
They inspect single events, and each sees one layer. DLP sees a commit, EDR sees a process; neither connects what an agent read to what it then did. We score the whole chain across the model, the tools, and the machine.
Yes — pilots start in observe mode: watch, log, and review the would-have-blocked list with its false positive rate on your own traffic. When you turn enforcement on, policies can warn before they block, or require a human in the loop. Blocking is per policy, when you decide.
The design fails open: a crash or overload never blocks a developer. Typical checks add under 1ms.
Action telemetry, not your codebase. Secrets are never stored; what we ingest is documented in the trust pack, and when we read from your gateway you control what telemetry we receive.
Yes — your SIEM can ingest the event stream, and our endpoint security sensors run alongside CrowdStrike-class EDR. Custom integrations are possible.
Native SIEM export plus alert routing to your queue or Slack with severity thresholds, agreed at scoping. Every alert arrives evidence-backed: the chain, the rule, the matched evidence.
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.
Rollout is a config push through the endpoint management tools you use, or a manual one-line config change. Start with one team and widen from there. A rollout that de-risks itself wins your developers’ trust, and the mistakes the platform catches help you bring engineering along: the platform fixes the thing they actually want fixed.
Yes. Every action is recorded and export-ready for audit.
