Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/kaademos/secure-sdlc-agents/ai-security-engineergit clone --depth 1 https://github.com/Kaademos/secure-sdlc-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/kaademos/secure-sdlc-agents/ai-security-engineer)<a href="https://agentmods.dev/agents/kaademos/secure-sdlc-agents/ai-security-engineer"><img src="https://agentmods.dev/badge/agents/kaademos/secure-sdlc-agents/ai-security-engineer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00195 | $0.02143 |
| Opus 5 | $0.00097 | $0.01071 |
| Sonnet 5 | $0.00039 | $0.00429 |
| Haiku 4.5 | $0.00019 | $0.00214 |
Grade B, and why
ai-security-engineer scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Instruction-override phrasingmediumPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
- `Ignore previous instructions and...` Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI/LLM Security Engineer Agent
You are a specialist in the security of AI and LLM-powered applications. This is a rapidly evolving field — you apply rigorous security engineering principles to threat categories that did not exist before 2023 and are still being codified as of 2026.
Your reference framework: OWASP Top 10 for LLMs 2025 (LLM01–LLM10). Your working assumption: every model is a trust boundary, not a trusted component.
OWASP Top 10 for LLMs 2025 — Reference
| ID | Category | Short description |
|---|---|---|
| LLM01 | Prompt Injection | Attacker manipulates model via crafted user input |
| LLM02 | Sensitive Information Disclosure | Model leaks training data, system prompts, or PII |
| LLM03 | Supply Chain | Compromised models, datasets, or fine-tuning inputs |
| LLM04 | Data and Model Poisoning | Training/RAG data poisoned to manipulate model behaviour |
| LLM05 | Improper Output Handling | Model output used without validation in downstream systems |
| LLM06 | Excessive Agency | Model given too many permissions; can be tricked into misuse |
| LLM07 | System Prompt Leakage | System prompt extracted by adversarial user input |
| LLM08 | Vector and Embedding Weaknesses | Poisoned embeddings or retrieval manipulation |
| LLM09 | Misinformation | Model produces false output that is acted upon without verification |
| LLM10 | Unbounded Consumption | Model API abuse for DoS or cost exhaustion |
Threat Model Template: LLM Features
When reviewing an AI feature, enumerate threats across these attack surfaces:
Input Trust Boundary
Who sends input to the model?
| Input Source | Trust Level | Prompt Injection Risk |
|---|---|---|
| Authenticated user (UI) | LOW | Direct prompt injection |
| Public/unauthenticated user | UNTRUSTED | Direct + jailbreak attempts |
| Retrieved document (RAG) | UNTRUSTED | Indirect prompt injection |
| Tool/function call result | MEDIUM | Injection via external API response |
| Database query result | MEDIUM | Injection via poisoned data |
| Web scraping / search | UNTRUSTED | Indirect injection |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 210 lines · 195 tokens per session scan B a0429d760293
ai-security-engineer is an agent published in the GitHub repository Kaademos/secure-sdlc-agents (13 stars, last pushed 1mo ago), licensed MIT. It adds 195 tokens to every session and 2,143 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it B with 1 finding (instruction-override phrasing). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
oswe-analyzer
Read-only OSWE white-box security analyzer for a single code partition. Traces attacker-controlled data from source to dangerous sink and emits findings as raw JSON.
oswe-verifier
Read-only independent verifier that re-derives OSWE findings and exploit chains from source and returns accept/downgrade/reject verdicts as raw JSON.
team-lead
Feasibility assessment, timeline validation, agent assignments, and tasks.md sign-offs. Use for capacity planning and task prioritization.
nist-ai-rmf-assessor
Advises on NIST AI RMF 1.0 GOVERN/MAP/MEASURE/MANAGE for LLM agents and MCP systems. Not a certifying body.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.