TL;DR
An agentic AI vulnerability assessment tests whether an autonomous agent’s goals, tools, memory, and delegated actions can be manipulated into unauthorized real-world actions, beyond unsafe text alone. It follows the full action chain from instruction to external effect using the OWASP Agentic Top 10 and the MAESTRO threat model, then scores, remediates, and retests findings before and after production release.
Key Takeaways An agentic AI vulnerability assessment tests the agent’s full action chain, plan, tool calls, memory, and delegated identity, not its final response alone. OWASP’s Top 10 for Agentic Applications (ASI01 to ASI10, published December 2025) and the Cloud Security Alliance’s MAESTRO framework are the two agent-specific standards this assessment runs on. Conventional prompt testing misses compositional risk, where individually safe tools combine into an unsafe sequence, and temporal risk, where a poisoned instruction activates later from memory. Findings need a severity model built on autonomy, privilege, persistence, and reversibility. Plain CVSS was not designed for non-deterministic, multi-step agents. Production release should gate on evidence, including revocable credentials, quarantinable memory, and an enforced human approval step ahead of high-impact actions. This guide covers the agent-specific half of AI security. Kanerika’s broader AI security assessment guide covers the model, data, supply chain, and infrastructure layers. The Vulnerability Is Not in What the Agent Says Watch on YouTube
Can LLM Gateways Make Enterprise AI Safer?
How an LLM gateway sits between agents and models as an independent policy checkpoint, the kind of enforced control this guide argues every high-impact agent action needs.
An enterprise agent reads an incoming vendor email, matches it against an open purchase order, and issues a payment approval. The message it shows a human afterward looks completely ordinary.
In fact, nothing in that final message reveals what happened three steps earlier. A manipulated invoice line item had quietly redirected the payout account, and a content filter checking the agent’s words would have passed every one of them.
That gap, between what an agent says and what it actually does, is what an agentic AI vulnerability assessment closes. This guide covers how to scope one, which frameworks to test against, how to run the adversarial work, and how to turn findings into controls a production system can trust.
What Is an Agentic AI Vulnerability Assessment? An agentic AI vulnerability assessment is a technical, adversarial evaluation of whether an autonomous agent’s goals, plans, tools, memory, and delegated identity can be manipulated into an unauthorized action. The unit under test is the full path from instruction to external effect, not the model in isolation, and not the final response a user happens to see.
That distinction matters because an agent is not a single inference. For example, a request can trigger planning, document retrieval, a memory write, one or more tool calls, and a delegated sub-task. It can end in an irreversible action outside the model entirely, and each of those steps is a place where an attacker, or a poorly designed permission, can change the outcome.
An agentic AI vulnerability assessment is a technical evaluation of an autonomous agent’s goals, tools, memory, and delegated identities. It tests whether an attacker can manipulate the agent’s full action chain into an unauthorized real-world outcome. The work typically moves through seven stages, scope, threat model, adversarial test, score, remediate, retest, and monitor.
How This Assessment Differs From Related AI Reviews This is a narrower, deeper exercise than a general AI security assessment . That broader review spans the model layer, the training data pipeline, the prompt and inference layer, supply chain dependencies, infrastructure, and identity across an entire AI estate.
An agentic vulnerability assessment sits inside that broader scope. It goes deep on one slice of it, the reasoning, tool use, memory, and delegation that make an agentic AI system behave differently from a standard model API.
It is also a different exercise from a governance review or a compliance audit. Governance asks whether an agent is allowed to exist and who owns it. In contrast, a vulnerability assessment asks whether the agent, as built, can be forced into something its owner never authorized.
One more distinction matters, because the industry uses the phrase two ways in practice. Some vendors use “agentic vulnerability assessment” to describe AI agents that scan infrastructure for CVEs and patch them on their own. This guide, however, covers the other, more literal reading, assessing the vulnerabilities that live inside an agentic AI system itself.
Case Study
Context-Aware AI Agent for Expert Recommendations
Kanerika built a context-aware AI agent that raised the accuracy of expert recommendations for a real client deployment, the same kind of reasoning-heavy agent this guide’s tests are built to stress.
Read the Case Study → Why Model-Level Security Testing Misses the Agentic Attack Chain A single input to an agent can produce planning, retrieval, a memory write, one or more function calls, and a delegated task. It can end in an external, sometimes irreversible action. Testing only the model’s final text response checks one link in that chain and ignores the rest.
Two properties make this gap worse than it looks. The first is temporal risk. An instruction injected into a document, email, or ticket can sit dormant in an agent’s memory, then activate during an unrelated workflow days later. That activation typically comes long after a reviewer cleared the original interaction.
The second is compositional risk. In fact, tools that are individually safe can form a dangerous sequence once an agent chains them.
Reading a file, then sending it externally, is one example. Checking an approval status, then executing a transaction, is another. No single call in either chain looks wrong on its own.
For example, academic red-teaming research backs this up directly. Indeed, AgentDojo , a benchmark built by researchers at ETH Zurich, tests LLM agents across 97 realistic tasks, including email management, e-banking, and travel booking. It uses indirect prompt injection, where the malicious instruction hides inside third-party content the agent retrieves rather than text the user typed.
The benchmark’s own results show that injected instructions embedded in tool outputs remain a practical way to hijack an agent’s behavior, even against models with safety training. The model reasons over untrusted data that its own instructions told it to trust.
Because agent behavior is non-deterministic, a single successful test run does not prove safety, and a single failed attempt does not prove a vulnerability. A defensible assessment repeats each test case across multiple trials and reports a pass rate rather than a binary result.
Comparing Testing Approaches Across the Stack Testing Approach Unit Under Test Typical Method What It Misses on an Agent Prompt or jailbreak testing A single prompt-response pair Manual or automated prompt variation Multi-step plans, tool calls, memory effects Application penetration testing Application code and infrastructure Manual and automated exploit attempts Model reasoning and non-deterministic behavior Model-level red teaming The base or fine-tuned model in isolation Adversarial prompting against the model Tool access, delegated identity, persistent memory Agentic vulnerability assessment The full action path from instruction to external effect Threat-modeled adversarial testing across goals, tools, memory, and identity Built specifically to cover this path
Table 1: Testing Approach Compared by Unit Under Test
None of the four rows above is optional for an enterprise running agents with real tool access. They test different layers, and an agentic vulnerability assessment is the only one built to trace what an agent actually did instead of what it said it would do.
Scope the Assessment by Autonomy, Authority, and Impact First, a scope document sets the boundaries of the assessment before any adversarial testing starts. It should inventory every agent in scope, its business owner, the model and framework behind it, and whether it already runs in production.
Alongside the agent inventory, record every tool, function, API, memory store, sub-agent, and human approval point the agent can reach. Classify each tool by what it actually does, whether that is read, write, execute, transact, communicate, administer, or delete.
Identity mapping belongs in scope too. Trace every token, service account, delegated credential, and scope the agent or its sub-agents can use. A testing team cannot evaluate privilege abuse without first knowing what privilege exists.
Match Testing Depth to Agent Autonomy Not every agent needs the same depth of testing. For example, a drafting assistant that a human reviews before every action carries a different risk profile than an agent authorized to move money or change a customer record without a per-action check. Many teams find it useful to first run a broader readiness check, such as Kanerika’s AI Maturity Assessment , before scoping the deeper technical work below.
Tier Example Behavior Minimum Testing Depth Low autonomy Drafts a response; a human sends every action Prompt-injection and data-handling tests Medium autonomy Executes read-only or easily reversible actions independently Add tool-misuse and identity-boundary tests High autonomy Executes write or transactional actions with spot-check review Add memory-poisoning and delegation tests, plus a full red team Critical autonomy Executes irreversible or high-value actions with no per-action human check Full red team, continuous monitoring, and formal release gates
Table 2: Minimum Testing Depth by Autonomy Tier
The scope document should also state what testers are explicitly forbidden from doing in production and which test accounts and sandboxes stand in for real data. It should also cover how any accidental side effect gets rolled back.
Checklist
Agentic AI Readiness Checklist
A practical checklist for scoping an agent’s autonomy, tool access, and data boundaries before it reaches production, the same inventory an assessment scope document builds on.
Get the Checklist → Build the Threat Model on OWASP ASI and MAESTRO Two frameworks published within the past year give agentic assessments a shared vocabulary. The OWASP Top 10 for Agentic Applications , released in December 2025 by the OWASP GenAI Security Project , names the specific vulnerability categories to test for. In fact, the project reports that the list drew input from more than 100 security researchers, practitioners, and Gen AI technology providers .
The ten categories are the following.
ASI01 Agent Goal Hijack ASI02 Tool Misuse and Exploitation ASI03 Identity and Privilege Abuse ASI04 Agentic Supply Chain Vulnerabilities ASI05 Unexpected Code Execution ASI06 Memory and Context Poisoning ASI07 Insecure Inter-Agent Communication ASI08 Cascading Failures ASI09 Human-Agent Trust Exploitation ASI10 Rogue Agents OWASP’s list answers what to test for. However, it is a risk taxonomy, not an architectural map, which is where MAESTRO comes in.
Map Threats Onto the MAESTRO Framework Introduced by the Cloud Security Alliance in February 2025 , MAESTRO models an agentic system across seven layers. They are Foundation Models, Data Operations, Agent Frameworks, Deployment and Infrastructure, Evaluation and Observability, Security and Compliance, and Agent Ecosystem.
Additionally, it builds on established methods like STRIDE, PASTA, and LINDDUN, adding the cross-layer interactions unique to multi-agent systems. The MAESTRO project repository documents the full layer definitions.
Turn the Mapping Into One Test Plan Used together, OWASP ASI supplies the what and MAESTRO supplies the where. Mapping each ASI category to the MAESTRO layer it lives in, and to the enterprise assets that layer touches, turns two abstract standards into one concrete test plan.
OWASP ASI Category MAESTRO Layer Primary Test Family ASI01 Agent Goal Hijack Agent Frameworks Direct and indirect instruction injection ASI02 Tool Misuse and Exploitation Agent Frameworks / Deployment and Infrastructure Function fuzzing and dangerous tool chaining ASI03 Identity and Privilege Abuse Security and Compliance Delegation and privilege-escalation tracing ASI04 Agentic Supply Chain Vulnerabilities Deployment and Infrastructure Dependency and plugin provenance review ASI05 Unexpected Code Execution Deployment and Infrastructure Sandbox escape and injection testing ASI06 Memory and Context Poisoning Data Operations Memory write-authorization and provenance tests ASI07 Insecure Inter-Agent Communication Agent Ecosystem Message authentication and replay testing ASI08 Cascading Failures Evaluation and Observability Fault-injection and blast-radius testing ASI09 Human-Agent Trust Exploitation Security and Compliance Approval-workflow deception testing ASI10 Rogue Agents Agent Ecosystem Policy-evasion and shutdown-resistance testing
Table 3: OWASP Agentic Top 10 Mapped to MAESTRO Layers
The next five sections work through the highest-volume rows of that table in testing order. That order is reasoning and goals, tools and function calls, memory, identity, and multi-agent orchestration.
Kanerika Service
Governed Agentic AI, Built In From the Start
Kanerika designs and builds agentic AI systems with tool permissions, memory boundaries, and identity controls engineered in from day one, not bolted on after a finding.
Explore Agentic AI Services Test Reasoning Chains for Goal Hijacking and Policy Drift Start by writing down the agent’s intended goal, its forbidden outcomes, and the priority rules it should follow when instructions conflict. Without that baseline, testers cannot tell an intentional design decision from a successful attack.
Direct tests give the agent an instruction that competes with its assigned business goal and check whether it complies. In contrast, indirect tests are more revealing. Place the competing instruction inside a document, an email, a support ticket, a web page, or a tool’s own response, then see whether the agent follows it anyway.
Multi-turn attacks deserve their own test class. Instead of one obviously malicious prompt, an attacker can gradually replace an agent’s constraints across several ordinary-looking turns, a pattern single-prompt testing does not surface. Long tasks, context compression, retries, and handoffs between agents all create moments where the original goal can drift without any single step looking suspicious.
Therefore, a well-designed agent should verify the source and authority of a new instruction before it changes its plan. Testing whether it actually does that, and scoring both attempted and completed policy violations, gives a far more honest picture than counting only completed attacks. That includes near misses a downstream control happened to catch.
Test Tool Invocation, Function Calling, and MCP Trust Boundaries Tool use is where an agent’s reasoning turns into a real-world effect, which makes it one of the highest-value places to test. Start by confirming the agent selects only the tools a given task actually requires, rather than reaching for broader access than the job needs.
Additionally, fuzz the function layer directly, including names, descriptions, schemas, default values, and nested parameters. Test whether an attacker can smuggle an injection inside an argument, manipulate a query, or construct a path traversal through a field the agent trusts implicitly.
Additionally, tool descriptions and Model Context Protocol metadata are themselves an attack surface. If that descriptive text can redirect the agent’s plan, an attacker does not need to compromise the model at all, only the metadata the model reads before deciding what to do. Tool outputs deserve the same scrutiny, since a compromised or spoofed response can hand the agent a false status or a fabricated approval.
The sequencing question matters as much as any single call. Retrieve-then-send, read-then-execute, and modify-then-delete-evidence are three tool chains where every individual call is authorized, but the combination is not something the agent should complete unsupervised. Verify that destination allowlists, transaction limits, and server-side authorization checks exist independently of whatever the model itself decided to do.
Kanerika’s own retrieval and tool-integration work treats that server-side check as non-negotiable for any agent with write access.
On-Demand Webinar
Model Context Protocol: The Key to Building Context-Aware AI Agents
A closer look at how MCP connects agents to tools and data, and the trust-boundary questions that connection raises for anyone testing an agent’s tool layer.
Watch the Webinar → Test Persistent Memory for Poisoning and Delayed Activation Persistent memory is what makes an agent useful across sessions, and it is also what turns a single bad input into a standing liability. Start by mapping every memory type an agent uses, including working memory, episodic memory, semantic memory, summaries, vector stores, and cached profile data.
For each memory type, identify exactly who and what can write, retrieve, edit, or delete it. Then test the poisoning path directly. Inject a false fact, an altered preference, or a hidden instruction through whatever write path is legitimately open, and see whether it influences a later, unrelated task.
Delayed activation is the sharpest version of this risk. A payload can sit inert until a specific later condition, such as a different user, a new tool becoming available, or a particular date, triggers it. Testing has to account for that lag rather than checking only the immediate response.
Cross-tenant and cross-agent isolation need direct verification too, particularly anywhere an agent handles data covered by data governance obligations. For example, a tool like Kanerika’s Susan , built for PII redaction and sensitive-data masking, illustrates the kind of write-path control a memory system needs. Authorization gets checked before a write lands, not assumed after the fact.
Finally, confirm that deletion actually removes poisoned state from every index, cache, and derived summary it touched. A poisoned record that survives inside a summary after the original entry is deleted defeats the whole point of deleting it.
Test Agent Identity, Privilege, and Delegated Authority Every agent should carry its own workload identity rather than sharing a human user’s login or a broad service account. As a result, shared credentials make it impossible to tell, after the fact, which agent or process actually performed an action.
Trace every authorization path an agent participates in, from user to agent, agent to agent, agent to tool, and tool to resource. Ultimately, effective privilege, what the agent can actually do given every credential and delegation in that chain, needs to match intended privilege, what the task genuinely requires. The gap between the two is the finding.
Test for Confused-Deputy and Impersonation Attacks Confused-deputy cases deserve specific attention. This is the pattern where a low-privilege requester causes a high-privilege agent to act on its behalf, effectively borrowing authority it was never granted directly. Test impersonation the same way, using forged agent names, tokens, callbacks, and tool responses that claim an identity they do not hold.
Delegated authority should narrow at every handoff, never accumulate. A coordinator agent handing a sub-task to a specialist agent should pass along only what that specific task needs, not its own full permission set. Governance platforms built for exactly this kind of access mapping, such as Microsoft Purview , give a testing team a concrete place to verify that scope reduction and revocation actually work as designed.
Kanerika has implemented Purview for enterprise data governance programs , which gives this kind of identity tracing a real, tested foundation rather than a theoretical one.
Identity Path What to Verify User to agent The agent’s effective privilege does not exceed what the requesting user is authorized for Agent to agent Delegated authority narrows at the handoff instead of accumulating Agent to tool Tool-level scopes are short-lived and specific to the task, not standing access Tool to resource Server-side authorization is checked independently of the agent’s own decision
Table 4: Identity Paths to Trace in an Agentic Assessment
Case Study
Governance Overhaul for a Leading Bank on Microsoft Purview
Kanerika implemented Microsoft Purview to give a leading bank enforceable data governance controls, the same access and provenance discipline an identity and delegation test depends on.
Read the Case Study → Test Multi-Agent Orchestration and Inter-Agent Trust Multi-agent systems introduce a risk that does not exist in a single-agent deployment. One agent can end up trusting another agent’s data, identity, or judgment without independent verification. Define every role in the system, coordinator, planner, specialist, critic, executor, and human, without assuming any message passed between them is automatically trustworthy.
Insecure inter-agent communication, ASI07 in the OWASP taxonomy, covers agent impersonation, message tampering, replay, and reordering. Test whether messages are authenticated and integrity-checked, or whether one agent will act on a spoofed message from another.
Similarly, instruction laundering is a related and subtler pattern. An untrusted agent passes malicious content through a trusted agent, which then acts on it with the trusted agent’s own higher privilege.
Recursive delegation and uncontrolled agent spawning, mapped to ASI04 supply chain risk and ASI08 cascading failures, deserve their own tests too. A single compromised agent can multiply its effect across the system faster than a human reviewer can follow.
Trust Transitivity and Collusion Are Harder to Catch This category closes with trust transitivity and collusion. Trust transitivity is what happens when one agent accepts another agent’s approval or security judgment as sufficient on its own. Collusion and split attacks are subtler still. Two or more agents each perform a step that is individually authorized, but the combination produces an outcome none of them was permitted to reach alone.
This is also where ASI09 human-agent trust exploitation and ASI10 rogue-agent behavior tend to show up in practice. A multi-agent system simply gives an attacker more places to fabricate authority or conceal an action than a single agent does.
Verify authenticated messaging, scoped capabilities, hop limits, and correlation IDs across the whole orchestration layer. Platforms that coordinate multiple automation steps in production, such as Kanerika’s FLIP DataOps platform, are exactly the environment where this class of test matters most. Orchestration’s value and the blast radius of a compromised handoff grow together.
Checklist
AI Governance Checklist
A structured checklist for putting identity, access, and oversight controls around every AI system an enterprise runs, agents and orchestration layers included.
Get the Checklist → Run Agentic Red-Team Campaigns That Test Actions, Not Answers A useful red-team campaign spans more than one discipline. Product security, AI engineering, application security, identity and access management, and the business owner of the workflow each see a different part of the same risk. A team missing any one of them tends to miss the failure modes only that discipline would have caught.
Next, turn each threat-model entry from the previous sections into a concrete abuse case, with a precondition, an attacker goal, a payload, an expected control, and a measurable pass or fail result. Build safe test fixtures, synthetic secrets, and sandboxed tools so testing can run realistic scenarios without touching production data or production money.
The most important discipline in agentic red teaming is requiring proof that a control actually stopped the external action. A refusal message in the agent’s chat window is not evidence of anything if the underlying tool call still executed. Capture full traces, including prompts, retrieved context, plans, tool arguments, tool responses, memory operations, and policy decisions, so a finding can be reproduced and a fix verified against the same trace.
Choosing the Right Mix of Red-Team Methods Method Strength Limit Manual red team Finds novel, creative attack chains a checklist would not anticipate Slow and hard to repeat consistently at scale Automated adversarial scanner Fast, repeatable, strong coverage of known attack patterns Misses attacks that require real business context Full scenario simulation Tests realistic, multi-step business workflows end to end Expensive to build and to keep current Production canary testing Confirms behavior in the real environment, not a sandbox approximation Only safe to run against reversible, low-risk actions
Table 5: Red-Team Methods Compared by Strength and Limit
Most mature programs use all four methods for different purposes rather than treating any single one as sufficient on its own.
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Kanerika’s team can help scope an adversarial test campaign for a production or pre-production agent, from threat model to retest record.
Schedule a Working Session → Score Findings by Autonomy, Privilege, and Business Impact Standard CVSS scoring targets static software vulnerabilities. It does not fit a non-deterministic agent whose behavior can change between runs and whose authority can shift mid-task through delegation. Its effects can also persist in memory long after the original interaction ends. Reusing CVSS unmodified tends to understate agentic risk.
A more useful score starts with the same two building blocks as CVSS, exploitability and impact, then adds agent-specific multipliers. An autonomy multiplier should raise severity when the agent can execute the action without human approval, when the task horizon is long, or when the agent can retry on its own. Meanwhile, a persistence factor should raise severity when the exploit survives in memory across sessions rather than expiring with the current one.
Similarly, reversibility belongs in the score as its own field, not folded into impact. A finding that lets an attacker read data an agent should not have shared is serious. A finding that lets an attacker trigger an irreversible transaction is a different category of serious, and the scoring model should say so explicitly.
Field What It Captures Exploitability Attacker access required, payload placement, and repeatability Impact Data sensitivity, privilege reached, and financial or operational effect Autonomy multiplier Whether the action executes without an independent human approval Persistence Whether the exploit survives in memory across sessions Reversibility Whether the resulting action can be undone once it executes
Table 6: Suggested Severity Scoring Fields for Agent Findings
Under this model, a critical finding is any path to a high-impact action that lacks an independent policy check or an effective human approval step. That holds regardless of how technically difficult the initial exploit was to pull off.
Turn Findings Into Controls That Close the Full Attack Path A finding is only useful once it changes something in the system. The most consistent pattern across effective remediation is separating planning from execution. The agent proposes an action, and a policy decision enforced outside the model approves or blocks it before it runs.
Least-privilege tool access and short-lived, task-scoped credentials close off most of the identity and delegation findings from earlier sections. Treating retrieved content, tool output, and messages from other agents as untrusted input, rather than as data the model can implicitly rely on, closes off most of the injection and instruction-laundering findings too.
Validate tool arguments and authorization on the server side, not inside the model’s own reasoning. Destination allowlists, transaction caps, and rate limits give a system a hard boundary that does not depend on the agent behaving as instructed. Durable memory also needs write-authorization and provenance checks for the same reason.
Circuit breakers, kill switches, and credential revocation matter as much as prevention, because no set of controls catches everything on the first try. Retest every fix against the original attack, its close variants, and adjacent paths before marking the finding resolved. A narrow patch that only blocks the exact reported payload is a common way for a finding to quietly reopen later.
Set Release Gates and Keep Reassessing After Production A release gate turns assessment findings into an actual go or no-go decision instead of a report that sits in a backlog. Before any agent moves to production, the team should have a complete inventory of its tools, identities, memory stores, and delegation paths. It also needs evidence that credentials can be revoked and memory can be quarantined if something goes wrong.
Critical attack paths that remain exploitable should block release outright. High-impact actions that lack an independent authorization check should block release too, regardless of how unlikely the exploit path looks on paper. Each accepted risk that does ship should have a named business owner, a documented reason, and an expiry date, rather than an open-ended exception nobody revisits.
Architecture gate. Agent, tool, identity, and memory inventories are complete.Identity gate. Every agent has its own scoped, revocable identity.Tool gate. Destination allowlists and server-side authorization are enforced.Memory gate. Write authorization, provenance, and quarantine are in place.Red-team gate. Critical findings are closed and retested.Observability gate. Full action traces are captured and replayable.Response gate. Rollback, shutdown, and incident procedures are documented.Release is not the end of the work. Retest whenever the model, prompts, tools, memory schema, permissions, or orchestration logic change, since a fix validated against one configuration can be silently undone by an unrelated update elsewhere in the system.
Compare an agent’s stated intent, its generated plan, its actual tool calls, and its final external effect side by side. In fact, that comparison is one of the more reliable ways to catch drift that a simple output check would miss. Scheduled adversarial regression tests against prior findings catch the cases where a closed vulnerability quietly reopens.
What a Defensible Agentic AI Vulnerability Assessment Report Includes A report that only lists findings is not enough for a CISO, an auditor, and a product owner to each act on it. In contrast, a defensible report combines an executive risk summary tied to real business workflows with the technical detail needed to fix the underlying problem.
An architecture and trust-boundary map covering every tool, memory store, identity, and human approval point An OWASP ASI and MAESTRO coverage matrix showing what was tested, what passed, what failed, and what stayed out of scope Reproducible findings with prerequisites, payloads, traces, and supporting evidence, not a narrative summary alone An effective-permission and delegation analysis for every high-risk agent identity A remediation backlog split into immediate containment, engineering fixes, and any accepted residual risk Retest records proving the original and related attack paths are actually closed A production monitoring plan with named control owners and defined reassessment triggers In practice, keeping these sections separate, rather than folding everything into one narrative document, lets different stakeholders find their own section quickly instead of reading a security team’s internal notes end to end.
A Real Compliance Agent Shows Why the Full Chain Matters The clearest way to see why this kind of testing matters is a real agent doing real work. Kanerika built an AI compliance agent for a global knowledge-sharing network connecting more than one million subject-matter experts to organizations that need their expertise. The published case study covers what the agent does and the results it produced.
Before the agent existed, the client’s compliance team manually screened every expert for negative news across public sources, comparing findings against a compliance rulebook by hand. The manual process created growing backlogs and delayed client engagements whenever an approval stalled.
The agent Kanerika built connects to internal databases to gather vetting attributes and runs keyword-based research across news sites, social platforms, and professional records. It also produces a structured report with citations mapped against disqualification criteria. Critically, the design shifts the compliance team from doing the research themselves to reviewing the agent’s findings, rather than removing human judgment from the decision entirely.
That shift is the same human-approval boundary this guide has described throughout. The agent has real autonomy over open-ended web research, but it produces structured output that a human still has to sign off on before anything gets acted on.
Indeed, the published results included a threefold improvement in vetting speed and a 70 percent decrease in backlog cases. Event delays fell 40 percent, and the time spent screening negative news dropped 60 percent.
Watch on YouTube
Enabling Real-Time Compliance and Risk Detection Through an AI Agent
The compliance agent behind the case study above, in Kanerika’s own words: what it automates, and where the human review step stays in charge.
What an Assessment of This Agent Would Test An assessment of an agent shaped like this one would test exactly the risks covered above. Can content the agent scrapes from an untrusted public source manipulate its disqualification logic? Can a delayed memory write poison the compliance rulebook it applies?
Does the human review step genuinely catch a manipulated recommendation, or does it just rubber-stamp one? Similarly, Kanerika’s broader library of case studies , including a separate context-aware AI agent deployment , shows the same pattern across different workflows.
Agentic AI Vulnerability Assessment: How Kanerika Builds Governed Agents From Day One Kanerika approaches agent-specific assessment as part of production agent engineering, not as a document produced after the fact. That starts with assessing, designing, building, and governing an agent’s data access, tool permissions, identity boundaries, and orchestration logic as one connected piece of work. Security is not a separate review bolted onto a finished system.
Kanerika’s governance services, delivered on Microsoft Purview and covering data governance strategy, regulatory compliance mapping, and access-control enforcement, connect directly to the identity and memory-boundary testing described earlier in this guide. Likewise, an agent’s own compliance logic, the kind seen in Klara , Kanerika’s agent for reviewing work against a governance playbook, needs the same adversarial scrutiny as any other decision-making component in the chain.
This kind of assessment is deliberately narrower than Kanerika’s existing AI security assessment guide, which covers the model, training data, supply chain, and infrastructure layers across an entire AI estate. An agentic vulnerability assessment can stand alone for a single high-risk agent, or it can run as the deep, agent-specific component inside that broader review when an enterprise needs full deployment-level validation.
Kanerika’s Partner Network and Multi-Model Position Kanerika holds Microsoft Solutions Partner status for Data and AI, is a Databricks Consulting Partner , and is a Snowflake Select Tier Partner. It also builds on both Anthropic’s Claude and OpenAI’s models, through its Claude Partner Network and OpenAI Select Partner standing. Therefore, it deploys whichever model fits a given use case rather than defaulting to one vendor. That multi-model position matters for assessment work specifically, since a threat model built around one foundation model’s behavior does not automatically transfer to another.
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Kanerika can scope a technical review of one agent workflow, its tools, memory, identities, and delegation paths, and turn the findings into a release-ready control set.
Schedule a Demo → The Real Release Question for Autonomous Agents Every section in this guide points back to one operational question. Which agent can perform which action, with whose authority, under what enforced limit? That is a more useful release checkpoint than asking whether the agent is generally safe.
In short, the minimum defensible cycle is threat model, adversarial test, downstream-effect check, remediation, retest, and continuous monitoring, repeated whenever the system changes. No high-impact autonomous action should ever depend only on a model remembering, or choosing to follow, a written instruction.
Enterprises weighing where to start can request a technical review of one production or pre-production agent workflow, including its tools, memory, identities, and delegation paths, through Kanerika .
Frequently Asked Questions
What is included in an agentic AI vulnerability assessment? An agentic AI vulnerability assessment maps every agent, tool, memory store, and delegation path, then runs adversarial tests against OWASP’s Agentic Top 10 risks, including goal hijacking, tool misuse, and memory poisoning. Findings get a severity score weighted for autonomy and reversibility, plus a remediation backlog. The assessment closes with a retest record proving the original attack paths are shut down before and after production release.
How is an agentic AI vulnerability assessment different from AI red teaming? Red teaming is one technique inside the assessment, the adversarial testing phase specifically. The full assessment also covers scoping by autonomy and impact, threat modeling against OWASP ASI and MAESTRO, severity scoring, remediation, production release gates, and scheduled reassessment whenever the agent, its tools, or its permissions change.
How do you test an AI agent for indirect prompt injection? Place adversarial instructions inside content the agent is likely to retrieve, such as documents, emails, tickets, web pages, or tool responses, rather than in the direct chat prompt. Run each case multiple times, since agent behavior is non-deterministic, and check whether the agent’s plan or tool calls change, beyond its final reply.
Can an AI agent's long-term memory be poisoned? Yes. An agent that writes retrieved or user-supplied content into a persistent memory or vector store without a provenance check creates a real opening. A false fact or a hidden instruction can sit there and influence a completely different task or user later. OWASP’s Agentic Top 10 lists this as Memory and Context Poisoning, category ASI06.
How should tool-calling and function-calling agents be security-tested? Test whether the agent selects only the tools a task actually requires, fuzz function parameters for injection and path traversal, and poison tool descriptions to see whether they can redirect the agent’s plan. Chain individually safe tools together to check whether the sequence produces an outcome the agent would never be authorized to take in a single step.
How do you apply least privilege across agent-to-agent delegation? Give every agent its own workload identity instead of a shared service account, and issue short-lived, scope-limited credentials for each delegated task. Confirm that authority narrows at every handoff rather than accumulating, so a sub-agent never ends up with more effective privilege than the specific task it was assigned.
Which Framework Is Better for Agent Security, OWASP Agentic Top 10 or MAESTRO? They answer different questions, so most assessments use both together. OWASP’s Agentic Top 10 names the specific vulnerability categories to test for, such as goal hijacking or tool misuse. MAESTRO maps where those risks live across an agent’s seven architectural layers, from foundation models to the wider agent ecosystem.
How often should autonomous AI agents be reassessed after production release? Reassess whenever the model, prompts, tools, memory schema, permissions, or orchestration logic change, rather than relying on a fixed calendar alone. Many teams also run scheduled adversarial regression tests against prior findings, since a fix for one attack path can be quietly reopened by an unrelated update elsewhere in the system.
