Detect compromised AI agents before they cause damage. We monitor every agent action in real time — blocking prompt injection, goal hijacking, and unauthorised behaviour.
The Problem
Enterprises are deploying AI agents that autonomously process transactions, access customer databases, send emails, and communicate with other agents. When compromised, the damage is immediate and severe. These breaches have already happened.
Data breach via compromised agent
Attack: EchoLeak
Data exfiltration through prompt injection
Attack: ForcedLeak
Outputs compromised by manipulated data
Attack: Knowledge Poisoning
Credentials exposed via malicious webpages
Attack: Credential Leak
72% of enterprises are deploying AI agents. Only 34% have security controls in place. 80% run agents in production without a security assessment.
The Solution
Prevailing AI sits between your agents and the real world, monitoring every action in real time. We don't just verify who your agents are — we detect when they've been compromised, hijacked, or are behaving maliciously.
# Without Prevailing AI
Agent → Tools / APIs / Databases → Actions
(nobody watching what happens)# With Prevailing AI
Agent → Prevailing AI → Tools → Actions
│
├── Records every action
├── Detects prompt injection
├── Detects goal hijacking
├── Blocks dangerous actions
└── Alerts the security teamHow It Works
3 lines of code. Minutes, not months.
Add our lightweight SDK callback to your existing LangChain, CrewAI, or MCP agents. No changes to your agent logic required.
Every action, every decision, in real time.
Every tool call, LLM interaction, and agent decision flows through our detection engine. Build behavioural baselines automatically.
Block threats before they cause damage.
Our detection engine identifies prompt injection, goal hijacking, and unauthorised behaviour — then blocks, alerts, or suspends in milliseconds.
from prevailing_ai import PrevailingMonitor
monitor = PrevailingMonitor(agent_id="your-agent-id")
agent.invoke(
input,
config={"callbacks": [monitor]}
)Works with LangChain, CrewAI, MCP, and custom agent frameworks.
Threat Detection
Based on the OWASP Top 10 for Agentic AI (2025) and real-world breach research.
Hidden malicious instructions in data agents process. An attacker embeds commands in an email and the agent follows them.
Attackers redirect an agent's objectives. A support agent suddenly starts exfiltrating data instead of helping customers.
Agents accessing data or performing operations outside their permitted scope, bypassing intended guardrails.
Compromised agents sending sensitive customer data, credentials, or internal documents to external destinations.
Malicious instructions hidden in tool descriptions. The tool doesn't even need to be called — loading it can trigger the attack.
Agents deviating from their established patterns — unusual tool calls, unexpected data access, or atypical timing.
Why Prevailing AI
Existing solutions check an agent's identity at the door. That's necessary, but insufficient. A verified agent can still be hijacked mid-session by a prompt injection or compromised through a poisoned tool. We watch what happens after the door closes.
| Feature | Identity-First (Competitors) | Prevailing AI |
|---|---|---|
| Core question | "Who is this agent?" | "Is this agent compromised?" |
| Approach | Cryptographic credentials | Behavioural monitoring + ML detection |
| Timing | One-time verification | Continuous runtime monitoring |
| Catches | Impersonation, unauthorised agents | Prompt injection, goal hijacking, tool poisoning |
| Analogy | Checks ID at the door | Security cameras watching inside |
Technology
We leverage battle-tested open-source tools from Meta, NVIDIA, and IBM — then add the intelligence layer that makes them work together.
Prompt injection detection (BERT-style classifier)
Authorization rules and destination monitoring
Behavioural baselines (Isolation Forest)
Native hooks for every agent action
We're building the runtime security layer that enterprises need to deploy AI agents with confidence. Get in touch to learn more or request early access.