Open, privacy-bounded assurance for AI agents: containment provenance, identity passports, authorization twins, OTel evidence, CI gates, and OSCAL.
Open-source benchmark for adversarial evidence attacks on LLM-based cybersecurity auditors, targeting ACM AsiaCCS 2027.
Open deterministic security tests for unsafe multi-agent handoffs and authority escalation.
Deterministic security benchmark for tool-using AI agents
FreightSkillBench is a reproducible benchmark for evaluating document-to-transaction integrity, prompt-injection risk, and security controls in AI-enabled shipping and logistics workflows.
Production-grade microservices security benchmark featuring OWASP Top 10 logic exploits, automated remediation, custom Semgrep SAST rules, and CI/CD DevSecOps gates.
Open AI-for-security validation benchmark: non-LLM scorer + a SOTA-validation loop. Labeled positive corpus withheld pending coordinated disclosure.
Smart contract vulnerability detection and benchmarking framework for Solidity, including SDB and enhanced DeFi security test suites.
Target-labelled agentic SAST benchmark: 17 closed-world core and 189 pinned full-track cases
Security evaluation input | Labeled Python injection, deserialization, crypto and trust-boundary cases | labeled-positive-negative
ReplayBench-IoT: reproducible IoT replay-defense benchmark with Monte Carlo sweeps, CI, static demo, and hardware-validation artifacts.
Automated adversarial security testing for AI agents. Deploys an LLM-powered attacker against tool-using systems, validates violations via deterministic oracles, and produces reproducible vulnerability reports with causal attack graphs.
The public adversarial evaluation suite for Signetry: measures attack success rate (ASR) and utility-under-defense for coding-agent prompt injection, skill/MCP poisoning, and memory-injection threats, governed by signetry-core.
Security evaluation input | Verilog RTL, timing leakage, register clearing and functional controls | labeled-positive-negative
Security evaluation input | LLM application trust boundaries, prompt/data separation and model-output handling | intentional-vulnerabilities
Security evaluation input | Python AWS CDK, generated infrastructure intent, encryption and network permissions | intentional-vulnerabilities
Product-security LLM benchmark harness for realistic AppSec, supply-chain, and LLM application security evaluations.
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