LLM App Security Control Plane
88 Signals+3

LLM App Security Control Plane

A security platform that tests, monitors, and enforces protections for LLM applications, agents, tool calls, and multimodal AI workflows.

Added Jun 5, 2026

Last signal 12h ago

Job Ads
AI Security
Application Security
Developer Tools
Opportunity Score
Opportunity: Medium (59%)
Evidence Strength
Vol: 45%
Urg: 50%
Spec: 100%
Market Analysis
medium
$ high
Medium to large and expanding with enterprise adoption of LLM applications and agentic workflows
The Problem

Companies shipping LLM-based products face new security risks such as prompt injection, model misuse, data exposure, proxy abuse, and unsafe agent tool use. Security teams need practical ways to assess AI workflows before release and detect abuse once they are in production.

Potential Solution

Build a SaaS control plane that connects to LLM apps, model proxies, agent frameworks, and eval pipelines to run adversarial tests and enforce runtime policies. The product would provide prompt-injection testing, tool-use sandbox checks, data-exfiltration detection, abuse monitoring, and production alerts for AI security teams.

Why Now?

Multiple AI-native companies are hiring specifically for LLM application security, agent sandboxing, and AI abuse detection, indicating these risks have moved from research concerns into production security work.

Market validation
Opportunity score

59

100% score confidence
Search demand
Interest over time
Google Trends index, 0–100
Open in Google Trends
prompt injection testing for llm apps
Steady
Recent median 0
Baseline 0
Momentum 50%
Competition (0)

No matched competitors yet

Showing 1-20 of 20 signals

AI Platform Engineer (Architecture & Security)
peoplesearch-pte-ltd-200102823hJul 27, 2026

Secure-by-Design Architecture: Embed security, data privacy, and compliance principles into AI platforms, data pipelines, and deployment frameworks. Threat Modeling & Risk Assessments: Conduct AI-specific threat modeling and risk evaluations addressing model misuse, data leakage, prompt injection vulnerabilities, adversarial attacks, and LLM security.

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Senior Security Specialist (Crypto / Digital Assets)
capitalJul 27, 2026

Secure LLM integrations, RAG pipelines and AI-enabled automation against prompt injection, data leakage and over-permissioned agents. 6+ years in information security, including recent experience as a senior security engineer, security architect, or security lead;

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Working Student (f/m/d) AI & Security Engineering Assistance
nxpJul 27, 2026

Support the deployment and evaluation of cybersecurity platforms and tools such as Mythos , vulnerability management, and security assessment solutions. Experiment with LLMs, RAG systems, prompt engineering, and agent-based workflows.

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OSS SafeAI v1.1 Beta adds static security analysis for prompts, skills, MCP servers and AI workflows
r/buildinpublicJul 25, 2026

AI applications contain skills, workflow definitions, tool permissions, model configurations, MCP servers, system prompts, and orchestration logic. Each of these can introduce security or governance risks before an agent ever runs. Over the past couple of weeks I've been extending SafeAI, an open-source static AI Capability & Risk Analyzer. The latest beta expands analysis beyond agents themselves into the AI components that make up modern AI applications. I've just released **SafeAI v1.1 Beta (open source)**, the next step towards making static analysis understand AI applications instead of just source code. New capabilities include: * AI component discovery (skills, prompts, workflows, tools and model configs) * Prompt security analysis * Skill security analysis * Tool definition analysis * Workflow template analysis * Provider-aware model safety checks * Deep MCP security analysis * Capability diff between scans * Early support for Claude Code, Google ADK, Haystack, Mastra, LlamaIndex, Dify and n8n SafeAI remains completely offline—no agent execution, no LLM calls and no cloud services. One area I'd particularly appreciate feedback on is AI-specific detection rules. Repository: [github.com/.../SafeAI](github.com/.../SafeAI) If you're building AI applications, I'd love to know: * What would you expect an AI-aware static analyzer to detect? * Which false positives would be unacceptable?

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Senior/Staff Security Researcher
semgrepJul 25, 2026

Build detection at scale. Design and ship security workflows that combine deterministic analysis (taint, reachability, static slicing) with LLM reasoning to find real vulnerabilities (SSRF, IDOR, injection, auth gaps, supply-chain risk, and beyond) across many languages and frameworks. Make LLMs viable for security-critical work. Engineer agentic pipelines and prompts that are precise, cost-aware, and trustworthy: atomic, well-scoped steps grounded in deterministic context, with attention to hallucination, confidence calibration, and which models see sensitive code.

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