Prompt Injection And Jailbreak Vulnerability Testing

via “automated red-team vulnerability scanning”

LLM prompt testing and evaluation — compare models, detect regressions, assertions, CI/CD.

Unique: Implements a modular attack strategy system where each vulnerability type (jailbreak, injection, prompt leaking, toxicity, bias) is a pluggable provider that generates test cases. Strategies can be composed and parameterized (e.g., 'crescendo jailbreak with 5 iterations'), and results are graded against guardrails (safety checks) to produce a structured vulnerability report.

vs others: Purpose-built red-teaming system integrated into evaluation pipeline (not a separate tool); supports custom attack strategies via plugins; generates reproducible adversarial test cases that can be version-controlled and shared

Meta's safety classifier for LLM content moderation.

Unique: CyberSecEval's prompt injection benchmark includes both textual and visual injection vectors (v3+), with multilingual variants (machine-translated MITRE prompts) and explicit measurement of false refusal rates, enabling more nuanced evaluation than binary safe/unsafe classification.

vs others: More systematic than manual prompt injection testing because it provides reproducible, quantified results across multiple injection techniques and models, and includes false refusal measurement which is often overlooked in simpler safety evaluations.

via “prompt-injection-resistance-testing”

Security toolkit for AI agents. Scan your machine for dangerous skills and MCP configs, monitor for supply chain attacks, test prompt injection resistance, and audit live MCP servers for tool poisoning.

Unique: Executes a curated library of prompt injection payloads against live agents and analyzes responses using pattern matching to detect successful exploits, providing quantified vulnerability metrics rather than just binary pass/fail results

vs others: More practical than manual red-teaming because it automates payload generation and response analysis, and more comprehensive than static analysis because it tests actual agent behavior under adversarial conditions

via “prompt-injection-and-jailbreak-technique-documentation”

A collection of GPT system prompts and various prompt injection/leaking knowledge.

Unique: Explicitly documents prompt injection and jailbreak techniques (e.g., GrokJailbreakPrompt.md) as part of the repository's educational mission, treating security vulnerabilities as learning opportunities rather than hiding them. The SECURITY.md file provides contribution guidelines for responsibly documenting vulnerabilities.

vs others: More transparent and educational than vendor security advisories that often withhold technical details, but less systematic than academic security research papers that provide formal vulnerability taxonomies and impact assessments.

via “prompt security and injection vulnerability detection”

Tool for prompt engineering.

via “runtime adversarial injection testing for agent vulnerability validation”

Unique: Implements agentic-specific adversarial payloads (prompt injections targeting tool selection, jailbreak attempts for guardrail bypass, malicious tool parameter injection) rather than generic fuzzing, enabling targeted testing of agent-specific attack surfaces

vs others: Provides proof-of-concept validation that static findings are actually exploitable, whereas pure static tools cannot confirm real-world impact; however, requires live agent access and isolated environments unlike static-only scanners

via “jailbreak-attempt-detection”