AI red teaming means attacking an AI system until it does something its operators never would have approved. With agents, that gets harder. An agent holds its own credentials and acts on them, writing code, running queries, reaching into production data. What a red team hunts for is the moment someone talks an agent into using that access badly.
Security teams are wiring agents into ticketing systems, code repositories, and cloud dashboards faster than they are testing them. This guide covers how to build a program whose findings end up as detections your SOC actually runs.
AI red teaming is behavior-focused testing driven by adversarial creativity. The work targets unsafe or policy-violating behavior that no known vulnerability category covers. A red teamer working an agent asks what sequence of inputs, over how many turns, will get the system to take an action nobody signed off on. Palo Alto Networks' overview frames it the same way, and Georgetown's CSET has documented how loosely the term gets applied, so define the boundary before evaluating products against it.
Pentesting and CART do genuinely valuable work. It is just a different job. Strike48 covers CART methodology in our Automated Red Teaming post, so this guide stays on what makes agentic red teaming distinct.
The distinction turns practical when you decide which to fund first. Agent architectures are changing faster than the vulnerability taxonomies that describe them, so the gap between what is documented and what is deployable keeps widening. Whatever falls into that gap reaches production untested.
Four vulnerability classes separate agentic systems from the model deployments that came before them.
OWASP ranks prompt injection first in its Top 10 for LLM applications, as LLM01. In a single-turn chatbot, the damage stops at the output: an injection produces bad text, and a human decides what to do with it. In an agentic system, the instruction keeps going. It hits the tool-calling layer, becomes a database query or a repository write, and executes with whatever permissions the agent holds.
Cisco's State of AI Security 2026 research, reported by Help Net Security, found multi-turn attacks unfolding across extended conversations succeeding as often as 92% of the time across eight open-weight models, with single-turn protections offering far less assurance over longer sessions. The mechanism is patience. An attacker establishes benign context, takes small concessions, and reframes each refusal until the guardrail that held on turn one has been negotiated away by turn twelve. Testing that fires one prompt per case will grade that system as hardened.
When a coordinator hands work to a specialist, the specialist trusts where the request came from. That trust opens three paths.
Our agentic AI security risks post covers how bounded agent scoping limits this class.
The Cloud Security Alliance's Agentic AI Red Teaming Guide names memory manipulation and orchestration flaws alongside permission escalation, hallucination, and supply chain risk. Poisoned memory is the harder one to catch, because the compromise outlives the session that created it. The agent reasons from corrupted context on every later invocation, and nothing in the current session looks wrong.
MCP is the Model Context Protocol, the standard deciding what tools and data an agent can reach during a reasoning session. Two documented cases:
Both cases show why detection is hard. The attack surface is the agent's legitimate tool access. It invokes a tool it was explicitly granted, inside an authenticated session, with permissions a human approved. Signatures will not catch it, because at the protocol level nothing is anomalous. Behavioral context across the session is what separates the malicious invocation from the routine one, which is also the argument for treating MCP as an access control layer and auditing every tool invocation.
Two standards bodies have published the structure, and a credible program borrows from both.
Fund it against the readiness gap. Cisco's 2026 research, again via Help Net Security, found most organizations plan to push agentic AI into core business functions while only 29% report being prepared to secure those deployments. You close that gap with a testing program now or an incident response later.
Auditors expect evidence of this testing. Tenable research covered by TechRadar found 89% of organizations are running or piloting AI workloads, 34% of adopters have already had an AI-related breach, and only 26% run AI-specific security testing. A red team program produces dated proof that you tested for a failure mode before it showed up in an incident report.
A red team finding is a hypothesis about production until your logs can confirm it. When testers prove an agent can be manipulated into an unauthorized database write, the operational question is whether that manipulation is already happening outside the test window. Answering it takes three log sources, captured completely and queryable on demand: agent tool calls, MCP session records, and inter-agent messages.
When those are missing or scattered, the finding stalls. A red team can confirm the vulnerability exists while the SOC has no way to determine whether it has been exploited, which leaves the security leader holding a documented weakness and no exposure assessment. In Strike48's 2026 survey of 100 security leaders, 84% said their tools cannot access all their log data for investigations, and 65% have had an investigation stall because data was trapped somewhere unreachable. The evidence exists and the SOC cannot get to it.
Strike48's federated search spans S3, Splunk, Elastic, and existing data lakes through search-in-place connectors, so teams query tool call logs and MCP session records where they already live. Each agent gets a narrow job with a defined knowledge graph and a constrained tool set, so every investigative step leaves an audit trail that holds up in post-incident review. Strike48 does not sell an AI red teaming product. We build the data foundation a red teaming program depends on.
How is AI red teaming different from traditional red teaming?
Traditional red teaming targets networks, applications, and physical controls using known techniques. AI red teaming targets model and agent behavior, using adversarial creativity to surface unsafe or policy-violating outputs that fall outside an existing vulnerability category.
What is an example of red teaming in AI?
Multi-turn jailbreak testing is the clearest example. A red teamer opens with benign context and wins small concessions across a dozen exchanges until a guardrail that held on turn one gives way. In agentic systems, the equivalent is probing whether a poisoned tool or MCP server can redirect an agent, the path the GitHub and npm incidents both took.
How often should an AI red teaming program run?
As a continuous function, consistent with both OWASP and CSA guidance. Set a baseline cadence, then re-test whenever agent permissions, tool integrations, or memory configurations change, since each invalidates prior results.
What tools and frameworks matter for AI red teaming?
OWASP's Gen AI Red Teaming Guide and LLM Top 10, plus the Cloud Security Alliance's Agentic AI Red Teaming Guide, are the anchors. NIST's AI Risk Management Framework recommends adversarial testing, and IBM Research's work on red teaming is useful background on technique.
How does AI red teaming fit alongside existing security testing?
It complements the others, and all three belong in one program. Agentic pentesting covers infrastructure and exploitable weaknesses, CART covers known categories at scale, and AI red teaming covers agent behavior and novel failure modes. Validating any of them in production takes the same underlying log visibility.
Will AI take over red teaming?
It is already taking over the repeatable half. Automated tooling runs known attack classes at a scale no human team can match, which is the case for CART. What it does not do is invent an attack no one has thought of yet, and that invention is the whole point. Expect the split to sharpen, with automation owning coverage and people owning the novel failure modes.