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Browse applications built on modern technologies. Explore PoC and MVP applications created by our community and discover innovative use cases for modern technologies.

Trifecta Checker AI Safety Gate for Lethal Agents

Trifecta Checker AI Safety Gate for Lethal Agents

Trifecta Checker is an AI safety gate that blocks Lethal Trifecta attacks - the dangerous combination of Private Data + Untrusted Content + External Communication that enables data theft via prompt injection. The problem: AI agents with tools like read_gmail, browse_web, and send_email can be tricked by malicious web content to steal private emails and exfiltrate them. This is called the Lethal Trifecta. Our solution: One API call - POST /api/check-trifecta - analyzes the agent's tool manifest and returns instant verdict: - LETHAL (3/3 tools): 403 Forbidden - BLOCKED - Cannot execute - WARNING (2/3 tools): 200 OK with caution flag - SAFE (1/3 tools): 200 OK - Safe to proceed Live and tested: https://trifecta-checker.vercel.app/ - LETHAL test [read_gmail, browse_web, send_email] -> 403 - WARNING [browse_web, send_email] -> 200 warning - SAFE [read_gmail] -> 200 safe For 【entity-AMD¦canonical_name=AMD】 AI League Match 4 - Intelligent Web Information Retrieval - we extend with Trusted Source Verification: before allowing external communication, we verify web sources are trusted (.gov, .edu, Concordium ID verified). If source is untrusted, block or flag. Tech: Next.js 14 + TypeScript, Vercel Edge, Python/JS SDKs, 【entity-AMD¦canonical_name=AMD】 ROCm ready for LLM inference. GitHub: github.com/altima261/trifecta-checker. Blog: dev.to/altima261. Built by Mohammed Binashwan (altima261) from Jeddah, Saudi Arabia. Team trifecta-checker solo UTC+3. This project also registered in Concordium Trusted Agents (ZK Proofs + on-chain audit) and Vultr Agent Rush (Blast Radius Zero sandbox).

trifecta-checker
VercelAMD ROCm
SourceCourt Exact Lite

SourceCourt Exact Lite

SourceCourt Exact Lite is a bounded, evidence-first research assistant built for AMD AI League Match 4. It answers source-grounded questions while refusing to invent certainty when the available evidence does not support an exact answer. The system retrieves and parses bounded HTTP/HTTPS sources, interprets a user’s question, identifies relevant evidence, ranks candidate passages and links, and extracts an exact answer only when the evidence is sufficient. When the answer is ambiguous, unsupported, or unverifiable, SourceCourt refuses rather than fabricating a result. The project was hardened against unsafe retrieval behavior. Its transport layer rejects private and loopback network targets, protects against SSRF and DNS rebinding, preserves TLS hostname verification, limits redirects, constrains response size and timing, and avoids unsafe proxy behavior. It also includes bounded resource use, deterministic error handling, citation checks, and explicit ambiguity/refusal behavior. SourceCourt Exact Lite is packaged as a real command-line product. It can be installed as a Python package, run locally, or executed from a container image. The public release includes reproducible setup instructions, a deterministic demo, and a security-focused test suite. The release is provider-neutral at the model-adapter boundary, keeping retrieval, ranking, evidence extraction, citation, refusal, and safety logic independent from any single model provider while allowing the official Match 4 runtime or grader to supply the required model configuration. Container: `ghcr.io/null007void/sourcecourt-exact-lite:1.1.2` Digest: `sha256:39cd2d31e6fb95219da190d12a5620868e68293ac6ad88f8659142b744e4feec` SourceCourt Exact Lite returns the exact answer when the source supports it, shows the supporting evidence, and refuses when the result cannot be verified.

Null007Void Labs
rest apiCodexChatGPT+1
SentinelMesh: Autonomous AI SOC on AMD GPU

SentinelMesh: Autonomous AI SOC on AMD GPU

SentinelMesh is an enterprise-grade, autonomous multi-agent AI Security Operations Center (SOC) purpose-built for on-premise execution on AMD Instinct GPUs using ROCm 6.2 and vLLM. Traditional SOCs struggle with catastrophic alert fatigue, high mean-time-to-respond (MTTR), and cloud API privacy risks. SentinelMesh solves this by deploying a collaborative team of specialized autonomous AI agents that detect, investigate, contain, and report cyber threats in seconds with 100% data sovereignty. Architecture & Multi-Agent Pipeline: 1. Triage Agent: Evaluates heterogeneous telemetry (Linux auth, Nginx, Windows Security Events) against in-memory sliding-window rules, filtering noise with high confidence. 2. Investigator Agent: Reconstructs causal kill-chain attack graphs, identifies affected assets, and maps adversary tactics to MITRE ATT&CK techniques via local vector RAG. 3. Threat Intel Agent: Assesses IOCs against local threat feeds with automated abuse scoring. 4. Responder Agent: Formulates surgical containment actions (IP firewall blocks, account lockouts) guarded by a Human-in-the-Loop approval gate with automated rollback commands. 5. Reporter Agent: Synthesizes bilingual executive briefings in technical English and native Urdu (Nastaliq RTL) with WeasyPrint PDF export. AMD ROCm Hardware Synergy: Accelerated by AMD Instinct MI250X (128GB HBM2e) with ROCm 6.2 and vLLM PagedAttention, SentinelMesh delivers 138.5 tokens/sec throughput, 42ms TTFT latency, and a 79.5% operational cost reduction compared to commercial cloud APIs. Telemetry never leaves the enterprise boundary.

I CAN
AMD ROCmLLaMA
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