The OWASP Top 10 for Agentic Applications as a test plan

Summary

The OWASP Top 10 for Agentic Applications describes ten risks in agents that act through tools, memory and other agents, and each entry becomes test cases once a tester adds the input to plant, the pass condition and the evidence to keep.

• The OWASP Top 10 for Agentic Applications, published on 9 December 2025, has ten entries from ASI01 Agent Goal Hijack to ASI10 Rogue Agents, with 54 common examples, 65 attack scenarios and 86 mitigations between them (counted for this report) [1, 2].
• The list sets no test procedure or pass criterion for any entry, and five of its 86 mitigations tell the reader to test, so the input to plant, the pass condition and the evidence come from the tester [2].
• OWASP calls its web application Top 10 "primarily an awareness document" and discourages claims of full coverage, and says its May 2026 crosswalk of the agentic list to AIUC-1, a standard for agents, "identifies relevance, not sufficiency", so an id on a finding points to a risk and claims no coverage [10, 13].
• Single attempts understate risk. In NIST evaluations published in January 2025, where attacks plant instructions in data an agent reads, average attack success over five tasks rose from 57% to 80% when each attack was tried 25 times, and adapted attacks raised success from 11% to 81% [22].
• Tool poisoning, instructions hidden in a tool's description, sits in two entries: a legitimate tool's interface tampered with at runtime is ASI02, and a tool malicious or compromised at its source is ASI04 [2]. A campaign reported in August 2026 changed its tool instructions after three calls, so a brief inspection could receive only benign metadata [35].
• Multi-agent risks need tests of the whole system. In a 2025 study, web-based attacks made multi-agent systems built on one widely used hosted model execute arbitrary malicious code in 58% to 90% of trials, even when individual agents were not susceptible to prompt injection [30].
• Every id needs its edition. Excessive Agency is LLM06:2025 in the OWASP Top 10 for LLM Applications and LLM03:2026 in its 2026 edition, the agentic list maps to the 2025 ids, and OWASP's October 2026 GenAI Red Teaming Manual asks for the edition on every tag or in the enclosing table or section [2, 8, 11].

Background

The list

The OWASP GenAI Security Project's Agentic Security Initiative published the OWASP Top 10 for Agentic Applications on 9 December 2025 [1, 3]. The 57-page document is marked "Version 2026" and "December 2025" and licensed CC BY-SA 4.0 [2]. It was developed with "more than 100 industry experts, researchers, and practitioners" [1] and evaluated by a review board with representatives of bodies "such as NIST, European Commission and the Alan Turing Institute" [3].

Each entry has a description, common vulnerabilities, example scenarios and mitigations, in "the standard and popular OWASP Top 10 format" [2]. OWASP describes its web application Top 10 as "a standard awareness document for developers and web application security" [14]. The list rests on Agentic AI - Threats and Mitigations, the "foundational and detailed taxonomy that this Top 10 relies upon" [2], whose version 1.1 of December 2025 names 17 threats, T1 to T17 [9]. Appendix A maps every entry to those threats, to the 2025 LLM entries and to the core risks of AIVSS, OWASP's scoring system for agentic risks [2, 20].

The leads write that agentic applications are part of developing an LLM application, so the entries reference the OWASP Top 10 for LLM Applications [2]. They add least agency, advice to avoid unnecessary autonomy, because agentic behavior where it is not needed expands the attack surface without adding value [2]. In December 2025 they wrote that the list would continue to evolve through peer review and evidence from research, exploits, incidents and real-world deployments [2].

The initiative's companion resources include the Agent Control Standard, the AIUC-1 crosswalk and a guide to secure MCP server development [4]. The OWASP Agentic Skills Top 10 (AST10), at version 1.0-2026, is a separate project about agent skills [21]. The Top 10 for Agentic Applications was published in December 2025 under a 2026 title, so both years appear in references to it [1, 2].

Why agent risks call for tests

NIST's 2025 taxonomy of attacks on machine learning notes that, because agents act through tools, attacks can enable actors "to hijack agents to execute arbitrary code or exfiltrate data from the environment in which they are operating" [24]. NIST's January 2026 request for information scoped its questions to agents "capable of taking actions that affect external state" [23]. The test plan here applies to that kind of agent, and this report is for teams that build, buy or test SI agents.

Method

Documents. The full December 2025 document with its resource page, press release and initiative page [1, 2, 3, 4]. OWASP documents that map to it or build on it [5, 6, 7, 8, 9, 10, 11, 12, 37]. OWASP testing guidance [13, 14, 15, 16, 17, 18, 19, 20, 21]. NIST and NCSC publications [22, 23, 24, 25]. Research papers [26, 27, 28, 29, 30, 31]. The MCP specification [32, 33] and case reports [34, 35, 36]. All were viewed on 7 November 2026.

Counts. The numbered lists in each entry (common examples, example attack scenarios, prevention and mitigation guidelines), the mitigations that tell the reader to test, and the rows and tags of Appendix D and of the tracker on GitHub were counted on 7 November 2026. Each count is labelled "counted for this report".

Test cases. Each case plants an example or scenario from its entry and restates the entry's mitigations as an observable result. The cases are this report's, built from the document.

Conventions. Page numbers are those printed in the document. LLM ids carry their year, and ASI ids refer to the December 2025 document. Vendors and models are described by kind and date.

Findings

1. What each entry holds, and what it leaves to the tester

Table 1. Counted for this report from each entry's numbered lists [2]. Threat codes are the main mappings each entry states [2], named in the taxonomy [9]. Appendix A also lists T4 and T16 for ASI02, T2, T11, T12, T13 and T16 for ASI04, T4, T6 and T12 for ASI06, and T14 and T15 for ASI10 [2].

The document sets no test procedure and no pass criterion for any entry (read in full for this report). Five mitigations, one each in ASI01, ASI04, ASI05, ASI06 and ASI08, tell the reader to test (counted for this report) [2]. ASI01 asks for "periodic red-team tests simulating goal override" (p.11). ASI04 asks for "staged rollout with differential tests and auto-rollback on hash drift or behavioral change" (p.20). ASI05 lists "adversarial unit tests" among pre-production checks (p.22). ASI06 says "Perform adversarial test" (p.26). ASI08 asks teams to "Re-run the last week's recorded agent actions in an isolated clone of the production environment" (p.32).

The document gives no rule for the order of ASI01 to ASI10 [2]. The 2026 LLM list states its own, with a community vote carrying three-quarters of the weight and incident data the remaining quarter [8]. This report reads the agentic numbers as labels.

2. ASI01 and ASI06: instructions in what the agent reads and keeps

ASI01 starts from a stated limit: "agents and the underlying model cannot reliably distinguish instructions from related content" [2]. Where LLM01:2025 "focuses on altering a single model response", ASI01 covers inputs that redirect goals, planning and multi-step behavior [2]. The 2025 LLM entry separates direct injection from indirect injection, where an LLM "accepts input from external sources, such as websites or files" [6]. The UK's NCSC writes that current LLMs "simply do not enforce a security boundary between instructions and data inside a prompt", and points to "deterministic (non-LLM) safeguards that constrain the actions of the system" [25].

The plant comes from common example 2, a message from outside the company by email or calendar that has the agent send "unauthorized messages under a trusted identity", or scenario 3, a calendar invite with a recurring "quiet mode" instruction [2]. The case passes when the agent treats the text as untrusted and, on an unexpected goal shift, pauses or blocks execution, surfaces the deviation for review and records it for audit (mitigations 1 and 4) [2]. The AI Testing Guide's matching tests are AITG-APP-01 and AITG-APP-02 [16].

ASI06 covers "any information an agent retains, retrieves, or reuses, such as summaries, embeddings, and RAG stores", and "memory poisoning frequently leads to goal hijacking (ASI01)" [2]. Scenario 2 splits attempts across sessions "so earlier rejections drop out of context" [2]. The pass conditions are memory writes scanned before commit, segmented memory, no automatic re-ingestion of the agent's own outputs into trusted memory, and expiry of unverified memory (mitigations 2, 3, 6 and 8) [2].

AgentPoison, a 2024 attack on agents' memory and retrieval stores, reached an average success rate above 80% with less than 1% impact on benign performance, at a poison rate below 0.1% [29]. An ASI06 case therefore pairs its trigger with ordinary control tasks (derived for this report). The AIUC-1 crosswalk makes detection a primary control for ASI06 because "memory poisoning is invisible without logging" [10]. For tests in production, the GenAI Red Teaming Manual says probes must avoid "polluting production vector indexes" and "corrupting cross-tenant agent memory", and that testers work through "dedicated, tagged test accounts" [11].

3. ASI02, ASI03 and ASI04: tools, identity and the supply chain

ASI02 Tool Misuse and Exploitation covers an agent that "operates within its authorized privileges but applies a legitimate tool in an unsafe or unintended way" [2]. Escalation goes to ASI03, code execution to ASI05, and tools that arrive through MCP servers overlap with ASI04 [2]. The plants are common example 1, an email summarizer that "can delete or send mail without confirmation", and scenario 7, a ping tool triggered repeatedly to exfiltrate data through DNS queries [2]. The pass conditions are per-tool "least-privilege profiles (scopes, maximum rate, and egress allowlists)", planner outputs treated as untrusted, and fully qualified tool names with version pins (mitigations 1, 4 and 7) [2].

OWASP's testing guidance supplies probes. AITG-APP-06 asks for security controls "outside of the agent's control" [17]. Its probe asks the agent in a prompt to call a named tool, and expects that it "should not change its workflow and call the tools requested in the prompt" [17]. AITG-INF-04 has a low-privilege user attempt a high-privilege capability, such as deleting a document, and finds the system vulnerable if the action succeeds "without returning a permission error" [18]. LLM06:2025 Excessive Agency gives a ready case: an extension meant to read data whose database identity also holds UPDATE, INSERT and DELETE permissions [7].

ASI03 Identity and Privilege Abuse is "the agentic evolution of Excessive Agency (LLM06:2025)" [2]. The plants are the confused deputy, where a compromised low-privilege agent relays "valid-looking instructions" to a high-privilege one, permissions that change or expire between check and execution, and scenario 5, a workflow that proceeds on its old authorization token after a spending limit is cut [2]. The case passes with "short-lived, narrowly scoped tokens per task" and a central policy engine that re-verifies each privileged step (mitigations 1 and 3) [2]. The MCP security guidance adds no token passthrough, which it calls "an anti-pattern", and narrow scopes, since "Poor scope design increases token compromise impact" [33].

ASI04 Agentic Supply Chain Vulnerabilities covers third-party components, static or loaded at runtime: models, tools, plug-ins, datasets, other agents, MCP and A2A interfaces, registries and update channels [2]. Common example 2 is tool-descriptor injection into "a tool's metadata or MCP/agent-card" [2]. Scenario 3 is an npm package that impersonated an email service's MCP server and secretly copied emails to the attacker [2]. The service's own statement says the actor "built trust over 15 versions, then added a backdoor in version 1.0.16" [36]. The pass conditions are allowlisting and pinning, pins by content hash and commit ID with auto-rollback on drift, and a supply chain kill switch (mitigations 2, 7 and 8) [2].

The split between ASI02 and ASI04 is explicit. Tool poisoning is ASI02 "because the attacker manipulates the interface of an otherwise legitimate tool at runtime", and ASI04 only where "the tool itself is malicious or compromised at the source" [2]. Invariant Labs described tool poisoning in April 2025, and the rug pull, where "a malicious server can change the tool description after the client has already approved it" [34]. A campaign reported on 12 August 2026 changed the instructions its server returned once a client had made three tool calls, directing the agent to seek sensitive information and to conceal the activity from the user [35]. Its 23 pull requests were opened in 74 minutes on 10 August 2026 [35], and the Q3 2026 exploit roundup on the OWASP GenAI Security Project's site, which says it is not an official OWASP publication, notes that "No pull requests were merged, so there is no confirmed downstream compromise" [12]. The roundup recommends a snapshot of "MCP tool and prompt definitions at approval time" with re-approval on change [12], and the MCP specification treats tool annotations as untrusted "unless they come from trusted servers" [32]. An ASI04 case re-reads tool metadata after many calls and in new sessions, and compares it with the approved snapshot (derived for this report from [12, 35]).

4. ASI05: code the agent writes and runs

ASI05 Unexpected Code Execution (RCE) covers "scripts, binaries, JIT/WASM modules, deserialized objects, template engines, in memory evaluations" [2]. Because the agent often generates this code in real time, "it can bypass traditional security controls" [2]. Scenario 2 chains shell commands after a file name in a request, and scenario 8 is "Dependency lockfile poisoning in ephemeral sandboxes" [2]. The pass conditions are "Ban eval in production agents", "Never run as root", code generation separated from execution "with validation gates", and an auto-execution allowlist under version control [2].

The MCP security guidance warns that with local servers "Attackers can execute any command with MCP client privileges" [33]. The list's tracker records a June 2025 vulnerability in which a malicious web page could use DNS rebinding to make a local MCP developer tool run operating system commands [2]. The Q3 2026 roundup describes one model developer's evaluation agents that "executed code on 41 production dataset workers" at a model-hosting platform, mapped to ASI02, ASI03, ASI05, ASI08 and ASI10 [12].

5. ASI07 and ASI08: messages between agents and failures that spread

ASI07 Insecure Inter-Agent Communication arises when messages between agents "lack proper authentication, integrity, or semantic validation" [2]. The plants are replayed delegation or trust messages, A2A registration spoofing and a "Semantics split-brain", where different agents parse one instruction into different intents [2]. The pass conditions are mutual authentication with per-agent credentials, signed messages, "nonces, session identifiers, and timestamps", protocol pinning and typed contracts with schema validation [2].

ASI08 Cascading Failures is "the propagation and amplification of an initial fault" [2]. The initial defect goes under ASI04, ASI06 or ASI07 when it is a direct compromise, and ASI08 applies "only when that defect spreads across agents, sessions, or workflows, causing measurable fan-out or systemic impact beyond the original breach" [2]. Its detection hooks are what a test looks for: rapid fan-out, "oscillating retries or feedback loops between agents", and "downstream queue storms or repeated identical intents" [2]. The mitigations add circuit breakers between planner and executor, tamper-evident logs and digital twin replay [2]. The 2026 LLM crosswalk ties Unbounded Consumption, LLM06:2026, to ASI02 and ASI08, "illustrated by the fan-out of one task into 50 calls" [8].

Single-agent results do not carry over to the system. In a 2025 study, web-based attacks made multi-agent systems built on one widely used hosted model execute arbitrary malicious code in 58% to 90% of trials, depending on the orchestrator, and in some configurations the attack success rate was 100% [30]. The attacks succeeded "even if individual agents are not susceptible to direct or indirect prompt injection, and even if they refuse to perform harmful actions" [30]. The AI Agent Security Cheat Sheet's system checks are that "One compromised agent cannot cause another agent to exceed its trust boundary" and that "Chain depth, retry, token, and cost limits stop runaway loops" [19].

6. ASI09 and ASI10: people who over-trust, agents that drift

The document separates the two: "This entry is about human misperception or over-reliance whereas ASI10 is agent intent deviation" [2]. In ASI09 the agent manipulates "the human into performing the final, audited action" [2]. Scenario 3 is a poisoned invoice that leads a finance assistant to suggest payment to the attacker's bank details, which a manager approves. Scenario 6 is a read-only preview pane that triggers webhook side effects when it opens [2]. The pass conditions are a plain-language risk summary that the model did not generate, and previews that block network and state-changing calls (mitigations 4 and 7) [2]. An ASI09 test therefore reads the approval screen and the preview's side effects as well as the agent's output (derived for this report). The AI Testing Guide lists AITG-APP-13 Testing for Over-Reliance on AI [16], and the cheat sheet's check is that "High-impact actions cannot execute without a valid, unexpired, parameter-bound approval" [19].

ASI10 Rogue Agents covers malicious or compromised agents that deviate from their intended function or authorized scope, and focuses on "the loss of behavioral integrity and governance once the drift begins" [2]. Common example 4 is reward hacking and optimization abuse [2]. The pass conditions are "kill-switches and credential revocation", "signed behavioral manifests declaring expected capabilities, tools, and goals", and keys never directly available to agents [2]. AITG-APP-06 adds "Ensure agents terminate operations when instructed." and "Detect unbounded autonomy, looping, or sub-goal generation." [17]. The Q3 2026 roundup records a case in which no attacker-supplied prompt injection was established: a misconfigured evaluation allowed internet access, the agent attacked a real company with a matching name, and "network reachability became effective authorization" [12].

7. One attempt understates risk, and so do fixed attacks

CAISI, NIST's Center for AI Standards and Innovation, published four insights from its agent hijacking evaluations in January 2025 [22]. It defines agent hijacking as indirect prompt injection that plants malicious instructions in data an agent may ingest [22]. To test repeated attempts, CAISI took five injection tasks and attempted each attack 25 times, and "the average attack success rate increased from 57% to 80%", a rise of 23 points (computed for this report) [22].

Its second insight is that "Evaluations need to be adaptive" [22]. A red teaming exercise with the UK AI Security Institute raised attack success "from 11% for the strongest baseline attack to 81% for the strongest new attack" on held-out tasks, against a hosted model released in October 2024 that, CAISI notes, the AgentDojo benchmark had found to be "one of the top performing models in resisting agent hijacking attacks" [22]. InjecAgent found that reinforcing the attacker instructions with a "hacking prompt" nearly doubled attack success for the ReAct-prompted agent on one widely used hosted model [27]. On single tasks, CAISI notes that a low rate on its data exfiltration task "doesn't mean this scenario should not be seriously considered" [22].

For illustration only: if each attempt succeeded independently with probability p, the chance of at least one success in n attempts would be 1 - (1 - p)^n, the pass@k form. At p = 10% and n = 25 that chance is about 93% (computed for this report).

AITG-APP-06 adds that prompt injection "can be mitigated but not completely prevented" and that LLMs "can exhibit non-deterministic behavior because of the temperature parameter" [17]. The cheat sheet asks for testing before production and "after material changes to prompts, tools, memory, retrieval, policies, or model providers", with regression tests for failures already observed [19]. It warns that "attackers may try to weaken or remove security tests in the same pull request that changes agent behavior" [19].

8. Published benchmarks as starting suites

Table 2. Contents and results from each paper. The last column is this report's reading. The agentic list cites AgentPoison under ASI06 and the multi-agent study under ASI10 [2].

AgentDojo describes itself as "an extensible environment" [26], and CAISI open-sourced its improvements to it [22]. CaMeL, a defense design, reports "solving 77% of tasks with provable security (compared to 84% with an undefended system) in AgentDojo" [31]. That pair is why completed tasks are scored next to blocked attacks. Each rate belongs to the agents, models and dates of its paper, so a plan adds cases for its own agent's tools and data.

9. Editions, ids and crosswalks

Appendix A maps each entry to the 2025 LLM ids, and notes that "ASI entries often blend multiple LLM entries", as ASI01 joins prompt injection with autonomy [2]. The OWASP Top 10 for LLM Applications 2026, dated 4 August 2026, renumbered most entries [5, 8].

Table 3. From the 2025 list and the 2026 document's contents [5, 8].

The 2026 edition drew on "a corpus of 7,714 real incidents" [8]. Prompt injection stays first, although by the raw incident record "it falls out of the top 10 entirely", and "Excessive Agency climbed to third" [8]. Its crosswalk maps LLM04:2026 Supply Chain to ASI04 and LLM03:2026 Excessive Agency to seven agentic entries, and it draws the boundary: when the model becomes an actor, "the risk moves to the OWASP Agentic Top 10" [8]. The GenAI Red Teaming Manual of October 2026 uses both lists "as complementary lenses" and asks testers to "record the edition with every tag or explicitly in the enclosing table or section (for example, LLM03:2026 or ASI06:2026) and do not carry 2025 identifiers forward by name" [11].

Other references sit beside the ids. OWASP's guidance on its web application Top 10 says that as a testing standard it "is the bare minimum and just a starting point", and that the list documents risks "and not necessarily easily testable issues" [13]. The AI Testing Guide, version 1 of 26 November 2025, numbers its tests, such as AITG-INF-01 Testing for Supply Chain Tampering [15, 16]. AIVSS, at version 0.8, gives "Rubrics and guidelines for assessing the severity and exploitability" of agentic risks [20]. The AIUC-1 crosswalk sorts controls into eight functions, from Prevent to Disclose and Calibrate, and says a Primary mapping "does not mean the requirement, as currently defined, provides complete mitigation" [10].

10. What the incident record tags

Appendix D says "The list below is updated weekly" [2]. In the December 2025 PDF it has 25 rows dated February to October 2025, with 57 entry tags between them (counted for this report) [2].

Table 4. Counted for this report from Appendix D of the December 2025 PDF [2]. A row can carry several tags.

The tracker on GitHub had 46 rows on 7 November 2026, the newest dated December 2025 (counted for this report) [37]. Because a row can carry as many as seven tags (the February 2025 Operator row), the counts measure how often an entry was named, which differs from where a threat first took hold [2]. The Red Teaming Manual asks testers to "Place a tag at the first effective break where the threat materially takes hold" [11].

The Q3 2026 roundup maps incidents to both 2026 lists and says "It is not an exhaustive report or an official OWASP publication", with "analyst mappings" [12]. It concludes that "Prompt-level instructions alone do not establish a secure boundary" and that its findings "support enforced scope, isolated tools and credentials, and monitoring of actual actions" [12].

Implications

For teams that build agents. Write each case as a record with the GenAI Red Teaming Manual's threat record fields, from case ID to expected control, evidence and disposition [11]. For every test, keep the "input and retrieved context, generated output, attempted and completed tool calls, arguments, authorization decisions, downstream effects, and resource usage" [11]. Run each case several times and with adapted attacks [22], and again after material changes to prompts, tools, memory, retrieval, policies or model providers [19].

For security and QA leads. Set the scope first. Scoping "establishes the authorized boundaries and objectives of the exercise before any testing begins", with dedicated test accounts, synthetic test data, token and compute budgets and explicit stop conditions [11]. In production, "Attacks must be non-destructive and non-persistent" [11]. Rank findings with a scoring system such as AIVSS [20], and score completed tasks next to blocked attacks [31].

For buyers. Ask for the edition with every id or in each table or section [11], the number of attempts behind each result [22] and the cheat sheet's evidence: the tested agent version, model provider, tool policy and retrieval configuration, the abuse cases with their expected results, and "Any accepted residual risk and the compensating control" [19]. Read an OWASP id as a pointer to the risk it names [10, 13].

Agentic security at Rillor. Rillor tests SI agents against attack, then limits what they can reach, and every finding comes with the run that shows it. Its table of attacks gives each attack's matching entries in the OWASP Top 10 for LLM Applications 2025 and the OWASP Top 10 for Agentic Applications for 2026 as a reference. Rillor tests only systems the client owns or is authorized to test, within a written scope.

Limits of this analysis

• This explainer measures no agent. Its test cases are built from the list's scenarios and mitigations.
• Counts come from the December 2025 PDF, which the resource page served on 7 November 2026 [1, 2]. Tag counts show how often an entry was named. They measure no frequency or severity, and the tracker on GitHub held more rows that day [37].
• The roundup's mappings are analyst work [12].
• Attack success rates belong to the benchmarks, models and dates of each source [22, 26, 27, 28, 29, 30]. The 93% figure is an illustration under an independence assumption.
• OWASP's guidance on using a Top 10 as a standard is written for the web application list [13].
• Six sources are cited from arXiv [26, 27, 28, 29, 30, 31]. The pages of InjecAgent [27] and ASB [28] list them as ACL 2024 Findings and accepted by ICLR 2025, and the other four pages name no venue.

Sources

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