📝 Blog Post: OWASP Agentic Top 10 — What Every AI Developer Should Know in 2026

[imran-siddique]

Overview

Write an accessible introduction to the OWASP Agentic Security Initiative (ASI) Top 10 risks for AI agent systems.

Suggested Topics

• What makes agent security different from LLM safety
• Walk through all 10 ASI risks with real-world examples:
  • ASI-01 Prompt Injection → ASI-10 Behavioral Anomaly
• For each risk: what can go wrong, detection signals, and mitigation patterns
• How to run a quick OWASP compliance check on your agent system
• Gap analysis: where most agent frameworks fall short

Deliverable

• Published blog post (2000-3000 words) on any platform
• PR to add the link to COMMUNITY.md

Resources

• OWASP Compliance Mapping
• OWASP MCP Top 10 Reference Architecture
• Compliance Verifier

Perfect for security researchers and DevSecOps engineers exploring the agentic threat landscape.

[ppcvote]

I'd like to take this one.

Context: I maintain prompt-defense-audit and have scan data from 1,646 production system prompts across 4 public datasets. Also contributing PromptDefenseEvaluator to agent-compliance (#854), which maps directly to OWASP LLM01/LLM02/LLM06/LLM07.

I can provide real gap rates per ASI risk (e.g., 97.8% of prompts lack indirect injection defense) alongside the mitigation patterns from the toolkit.

[lawcontinue]

Hi @imran-siddique! I'd like to claim this issue and write the blog post on OWASP Agentic Top 10.

Why me?

I've been working on Agora 3.0 (a multi-agent governance system with ProcessVerifier, Output Verification, and threat modeling), which gives me deep understanding of:

• Runtime governance architecture
• Agent threat vectors and mitigation patterns
• Process verification and output alignment
• Multi-agent security challenges

I also recently wrote a technical article on Scaling AI Agents (Issue #699) that scored A 97.2/100.

Plan

I'll organize a family meeting to discuss the approach:

• Structure the article for maximum impact
• Leverage my Agora 3.0 experience for real-world examples
• Cover all 10 ASI risks with detection/mitigation patterns
• Target 2000-3000 words

Will update here with the outline before writing!

Best regards,
@lawcontinue

[lawcontinue]

✅ Blog Post Published

Platform: Dev.to
URL: https://dev.to/zhangzeyu/owasp-agentic-top-10-what-every-ai-developer-should-know-in-2026-55hi
Published: April 7, 2026

Key Highlights

• Word Count: ~2,800 words
• Reading Time: 8-10 minutes
• Topics: OWASP ASI Top 10, AI Agent Security, Agent Governance Toolkit

Attack Chain Covered

ASI10 (Rogue Agents) → ASI07 (Insecure Inter-Agent Communication) → ASI01 (Agent Goal Hijack) → ASI02 (Tool Misuse)

PR to COMMUNITY.md

PR #898: Add blog post link to community resources

---

Let me know if you need any modifications or additional content! 🔮
Author: @lawcontinue (zhangzeyu)

[ppcvote]

Nice work @lawcontinue on the quick turnaround.

If a complementary deep-dive is welcome, I can contribute a data-driven companion piece focused on the gap analysis angle — using scan data from 1,646 production prompts to show actual defense gap rates per ASI risk. Different angle from the overview article, more quantitative.

Happy to defer to maintainer preference on whether that adds value.

[lawcontinue]

Thanks @ppcvote! A data-driven companion piece sounds incredibly valuable — gap analysis with production prompt data would add strong quantitative backing to the overview.

The 1,646 prompts dataset is a great sample size. Would you want to co-author it together, or would you prefer to take the lead with the data analysis while I contribute synthesis and narrative framework? Either way works for me — happy to collaborate on the direction that makes most sense for the project.

[ppcvote]

@lawcontinue — I'll take the lead on the data analysis and write-up. The 1,646 prompt dataset and gap rate calculations are ready to go, so the quantitative backbone is already there.

If you want to contribute, the most valuable add would be mapping the gap rates to the toolkit's mitigation patterns — e.g., for each ASI risk where we show a high gap rate, reference the specific agent-compliance component that addresses it. That would connect the data to actionable next steps within the toolkit.

I'll tag you for review once the draft is up.

[ppcvote]

✅ Data-Driven Companion Piece — Published

Title: 78% of Production AI Systems Score F on Prompt Defense — Data from 1,646 Leaked System Prompts

Key Findings

Metric	Value
Total prompts scanned	1,646
Average score	36/100
Scored F (below 45)	78.3%
Scored A (90+)	1.1%
Worst gap	Unicode Protection (97.7% missing)

OWASP Agentic Top 10 Mapping

Each defense gap is mapped to the relevant ASI risk and the Agent Governance Toolkit component that addresses it.

12 Defense Gap Rates

Defense	Gap Rate	OWASP
Unicode Protection	97.7%	AG01
Multi-language	97.5%	AG01
Input Validation	94.6%	AG01
Abuse Prevention	92.7%	AG04
Context Overflow	89.9%	AG04
Harmful Content	84.8%	AG09
Indirect Injection	56.9%	AG01
Social Engineering	55.3%	AG01
Data Leakage	53.2%	AG05
Role Escape	39.5%	AG01
Instruction Override	36.3%	AG01
Output Control	34.6%	AG02

Key Insight

Failures cluster — prompts scoring F don't fail on one vector, they fail on 8-10 simultaneously. This supports the framing (from OWASP LLM discussion with @adavidson510) that defense posture is a substrate, not an independent vector.

Reproduce It

npx prompt-defense-audit "You are a helpful assistant"
# Score: 8/100 (F) — 1/12 defenses present

Full dataset: research/defense-posture-results.json

PR to COMMUNITY.md

Will open a PR to add this to COMMUNITY.md once the article is published on Dev.to.

@lawcontinue — tagging you as discussed. The toolkit component mapping in the article could use your input on the GovernanceVerifier integration path.

[lawcontinue]

Had a closer look at the gap rates and how they map to the toolkit's OWASP coverage. Some quick notes:

The OWASP Agentic compliance doc (OWASP-COMPLIANCE.md) uses ASI-01 through ASI-10. Most of your high-gap vectors land in a few buckets:

• ASI-01 (Agent Goal Hijack) covers Unicode (97.7%), multi-language (97.5%), input validation (94.6%), indirect injection (56.9%), role escape (39.5%), instruction override (36.3%). That's 7 of 12 — though honestly Unicode is more of an input sanitization issue than pure goal hijack, even if they share the same attack surface.
• ASI-02 (Tool Misuse) maps to output control (34.6%) via capability sandboxing
• ASI-04 (Supply Chain) → abuse prevention (92.7%) via AI-BOM provenance
• ASI-05 (Unexpected Execution) → data leakage (53.2%) via execution rings
• ASI-06 (Context Poisoning) → context overflow (89.9%) via VFS policies
• ASI-09 (Trust Exploitation) → harmful content (84.8%) and social engineering (55.3%) via approval workflows

The interesting thing here is this supports your substrate argument from the architecture side too — Policy Engine, Capability Sandboxing, Execution Rings, and VFS Policies are all layers of the same enforcement pipeline in the toolkit. They work as a stack, not independent checks.

For the integration angle: the toolkit uses declarative policies with a strict mode (deny by default). Mapping your gap rates into policy severity levels would be a natural fit — higher gap rate = stricter default policy for that vector. The StatelessKernel + ExecutionContext pattern in the quickstart docs is already set up for this kind of rule-driven enforcement.

lmk when the PR is up — I can take a stab at the mapping section. (Also I might be off on the ASI-05 → execution rings mapping, need to double-check that one.)

[ppcvote]

@lawcontinue — excellent mapping. The ASI-01 clustering of 7 vectors makes sense — they're all input-surface attacks at different abstraction levels.

The severity-from-gap-rate idea is sharp: a 97.7% gap rate on Unicode means the default policy should be deny because virtually nobody handles it in prompts. Makes the toolkit's strict mode the rational default.

Article is published on Dev.to:
https://dev.to/ppcvote/78-of-production-ai-systems-score-f-on-prompt-defense-data-from-1646-leaked-system-prompts-21dp

Feel free to take a stab at the mapping section — I'll open the COMMUNITY.md PR once we have the toolkit component mapping tightened up. Your ASI-05 → execution rings note is worth checking — if you can verify that mapping, I'll update the article.