An open specification for agentic AI security evaluation and testing, from Cisco.
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Updated
May 12, 2026
An open specification for agentic AI security evaluation and testing, from Cisco.
LiteInjector : A LiteX based tool for emulating logical faults on Systems on Chips
Data and code to design and evaluate the PLL-based true random number generator according to the paper "Enhancing Quality and Security of the PLL-TRNG" (published and awarded at TCHES 2023).
Code of "SIA-GAN: Scrambling Inversion Attack Using Generative Adversarial Network" (IEEE Access 2021). Easy-to-use version: pip install scramblekit
Security Evaluation of NoSQL vs SQL Databases and Usability Studies of Opera Developer Browser using R Markdown and latex.
A Python evaluation comparing performance and bit-diffusion security (SAC) between XOR and XNOR implementations in AES across 6 block-cipher modes.
End-to-end PE malware detection with XGBoost and MalConv2. Adversarial robustness evaluation via GAMMA attack, SHAP interpretability, and multi-model Pareto comparison.
Structured findings for LLM agent and RAG vulnerabilities. Each scenario ships vulnerable, fixed, and hard-negative variants with traces, deterministic tests, and Semgrep rules.
Multi-agent security evaluation framework: SecureEval guardrails, intelligent agent routing, and consensus building for inspecting repositories for leaks, quality gaps, and documentation health.
Agent Hub by ming (ming8m): local multi-agent collaboration with cloud APIs, plus Chinese methods for authorized AI red-team evidence review.
Reproducibility package for auditable neural differential distinguishers on reduced-round lightweight-style block ciphers.
Evaluation harness for LLM vulnerability detection: labeled vuln/fixed dataset, regex + AST taint-tracking baselines, patch-quality proxy, and false-positive burden on human reviewers
A prompt-injection firewall that publishes its own bypass rate. Leave-one-family-out evaluation: hold out an attack technique and the countermeasures it motivated, then report how often it gets through.
Reproducible LLM agent security evaluation framework for testing prompt injection attacks, comparing defenses, and measuring security gains against capability loss using sandboxed tools, local models, and automated metrics.
Security evaluation datasets for DSH plugins
DSH plugin for agent observability and security evaluation
Reproducible lab for measuring prompt injection, RAG poisoning and data exfiltration risk across layered GenAI security controls
Measuring prompt-injection defences against policy-conformant attacks
Local Linux enforcement and security evidence for coding-agent evaluations with Inspect AI
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