Secure the agents
before they act.
Cogensec secures autonomous AI end to end, adversarial red teaming, structural integrity audits, and runtime defense: for AI labs and enterprises running agents in production.
What AI agent security actually covers.
AI agent security is the discipline of securing autonomous and semi-autonomous AI systems , LLM-powered agents, copilots and multi-agent workflows, against adversarial inputs, tool misuse, data exfiltration and behavioral drift.
Unlike traditional application security, it has to account for non-deterministic decision-making, emergent multi-step behavior, and trust boundaries that shift every time the model sees new input. The same model can be safe in one context and dangerous in another depending on the tools, data and identities at hand.
Cogensec delivers this as a single programme covering assessment, scoring and runtime defense: not point tools.
One programme, four stages.
The same lifecycle we run across every engagement, from first assessment to standing defense.
Assess
Map the agent’s tools, data, identities and trust boundaries, then pressure-test them adversarially.
Protect
Harden models, tools, identities and autonomous workflows before they reach production.
Operate
Monitor and govern agents in production, where behaviour drifts and the attack surface moves.
Assure
Score structural integrity and re-verify continuously, with evidence you can hand to an auditor.
Four service lines.
Run individually or as one continuous programme. Every engagement is paid, scoped and confidential.
AI agent red teaming
Adversarial assessments against your AI agents, prompt injection, tool abuse, memory poisoning, identity drift, and the lethal-trifecta combinations that bypass guardrails.
- Prompt and retrieval surfaces
- Tool and function-call chains
- Memory and RAG persistence
- Identity and privilege hops
You get: Reproducible, evidence-grade findings with severity, attack path and remediation.
Red Team NetworkRuntime defense
Policy-as-code runtime defense for agentic AI: every tool call, retrieval and outbound action evaluated against your security policy in real time.
- Tool-call authorisation
- Retrieval provenance
- Outbound action control
- Policy versioning and audit
You get: Enforced policy in your pipeline, plus the telemetry to prove it held.
Agentegrity audits
Structural integrity scoring for autonomous AI on three properties: Adversarial Coherence, Verifiable Assurance and Environmental Portability.
- Adversarial Coherence
- Verifiable Assurance
- Environmental Portability
- Signed attestation records
You get: A tracked score against the open framework, and the evidence behind it.
Agentegrity frameworkContinuous monitoring
Ongoing telemetry, drift detection and incident response for AI agents in production, not a one-off audit.
- Behavioural drift detection
- Live policy violations
- Incident response
- Re-scoring on change
You get: A standing programme with alerting, review cadence and re-verification.
Agentic AI risks we address.
Each of these is covered in depth in our published work.
Prompt injection & jailbreaks
Direct and indirect injection attacks that exfiltrate data or hijack agent goals.
AI Red Teaming Guide →Tool & function-call abuse
Agents tricked into invoking destructive tools or chaining tool calls outside scope.
CAAP v1.0 →RAG & memory poisoning
Adversarial documents and persistent memory entries that bias future decisions.
CAAP v1.0 →Identity & RBAC drift
Over-privileged service accounts and confused-deputy patterns across agent hops.
LLM Security 101 →Lethal trifecta
Untrusted input + sensitive data access + outbound communication, the combination that turns a small bug into exfiltration.
Agents Gone Rogue →Agent collusion
Multi-agent systems where one agent’s output becomes another’s trusted instruction.
Published research →How we secure AI agents.
Four steps, each with something that leaves the engagement.
Threat model the agent
Map tools, data, identities and trust boundaries, what the agent can touch and on whose behalf.
Agent threat model and scoped test plan
Red team the system
Adversarial pressure across prompts, retrieval, memory, tools and human-in-the-loop surfaces. Reproducible, evidence-grade findings.
Findings report with reproduction steps
Score with Agentegrity
Quantitative scoring across adversarial coherence, verifiable assurance and environmental portability, a single number you can track.
Agentegrity score and attestation record
Deploy runtime defense
Policy-as-code enforcement at runtime, so the risks you found stay caught when the system changes.
Enforced policy and monitoring cadence
Research-grade agentic AI security.
- Published research on agent collusion, semantic inversion and the Agentegrity framework, see Research.
- CAAP v1.0, our open taxonomy of 200 agent attack patterns across 11 domains, see the standard.
- An elite Red Team Network of AI-native and offensive security practitioners.
- The Cortex open-model series for cognitive-security research, in controlled release.
- NVIDIA Inception partner. Our controls are aligned to SOC 2 and ISO 27001.
How Organizations Work With Us
Choose the engagement model that fits your security maturity and operational needs.
AI Security Readiness Assessments
Time-boxed evaluations of exposure across identity, data, tools, and decision logic.
Adversarial AI Red Teaming
Live attack simulations against production or staging environments.
Continuous Protection Platform
Integrated security controls embedded directly into AI pipelines.
Government & Critical Infrastructure
Mission-aligned deployments focused on resilience, integrity, and national security.
AI agent security, answered.
What is AI agent security?
AI agent security is the discipline of securing autonomous and semi-autonomous AI systems, LLM-powered agents, copilots, and multi-agent workflows, against adversarial inputs, tool misuse, data exfiltration, and behavioral drift. Unlike traditional application security, it has to account for non-deterministic decision-making and emergent multi-step behavior.
What is agentic AI security?
Agentic AI security focuses on AI systems that take actions in the world, calling tools, querying databases, sending messages, executing code, not just generating text. The core problem is that the same model can be benign in one context and dangerous in another depending on the inputs it sees, the tools it has, and the data it can reach.
How do you secure AI agents?
In four layers: (1) threat-model the agent’s tools, data, and identities; (2) red team adversarially against prompt injection, tool abuse, memory poisoning, and identity drift; (3) score structural integrity with a framework like Agentegrity; (4) enforce policy-as-code at runtime so violations are caught when the system changes. Cogensec delivers all four as a single service.
How is AI agent security different from LLM security?
LLM security focuses on the model itself, prompt injection, jailbreaks, output safety. AI agent security has to cover everything LLM security does plus the agent’s tools, memory, RAG sources, identity, and multi-step trust boundaries. An agent fails in ways a stateless chat completion cannot.
What is the best AI agent security platform?
There is no single best platform, the right answer depends on your stack and threat model. Cogensec’s approach combines services (red teaming, audits) with runtime defense and a scoring framework (Agentegrity) so you get both the assessment and the controls in one engagement. For research-grade work on frontier systems, that combination is the differentiator.
Do you offer AI agent security services for enterprises?
Yes. Cogensec runs paid, scoped engagements for AI labs and enterprises deploying agentic AI in production, from launch-readiness assessments to continuous monitoring programs. Engagements are confidential and matched to your specialty area and stack.
Ready to secure your AI agents?
Confidential scoping. Paid, scoped engagements. Reproducible, evidence-grade findings.