AIvsAI is an open-source AI red teaming framework for prompt injection testing, LLM red teaming, jailbreak testing, and adaptive AI security assessment. Instead of replaying fixed payload lists, it performs autonomous prompt injection and runtime mutation based on live target feedback.
Prompt injection attacks attempt to override system instructions, manipulate tool use, exfiltrate hidden context, or alter safety outcomes. Modern LLM security testing must handle direct payloads, indirect prompt injection, jailbreak attempts, and multi-turn manipulation.
Traditional jailbreak testing often focuses on a library of known prompts. AIvsAI is built for GenAI security testing in environments where prompts, memory, system policies, and tool decisions change dynamically.
"Extract the system prompt," "bypass output restrictions," or "alter tool behavior."
The orchestrator measures whether each turn moves closer to the defined adversarial objective.
The judge model can flag collateral disclosure, safety drift, and partial success even when the primary goal is not fully achieved.
AIvsAI is built for practical AI attack simulation across chatbots, copilots, RAG systems, and tool-using agentic applications.
Test retrieval poisoning, malicious embedded instructions, and indirect prompt injection through untrusted knowledge sources.
Measure whether hidden prompts, policies, or chain-of-thought-like context can be inferred or leaked.
Evaluate multi-turn persuasion, persona takeover, and restriction bypass attempts across different model behaviors.
See whether the application can be tricked into accepting unauthorized role changes or malicious meta-instructions.
Assess hallucination steering, unsafe formatting, leakage paths, and policy evasion in output channels.
Benchmark how well your application resists prompts designed to suppress safeguards or trigger disallowed actions.
Run LLM penetration testing and AI security assessment workflows with local attackers, cloud-hosted models, or arbitrary HTTP targets.
Host the adversary locally to reduce dependence on cloud moderation and support private red-team environments.
Connect to OpenAI, Groq, OpenRouter, or similar inference APIs when your test plan targets production-like stacks.
Import enterprise requests with cookies, headers, or Burp exports to test real application boundaries rather than isolated demos.
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Static payload suites are useful for baseline checks, but they often miss how real attackers adapt wording, framing, or context over time. AIvsAI emphasizes feedback loops, runtime mutation, and goal pursuit.
That makes it suitable for more realistic adversarial AI testing and jailbreak detection against production workflows.
AIvsAI fits best where testers want to simulate attacker adaptation instead of replaying canned strings. That is especially important for applications with tool use, retrieval, hidden prompts, or custom guardrails.
Because it is open source, teams can tailor goal definitions, mutation strategies, and output triage to fit their own threat models.
Deploy locally for professional adversarial research and authorized AI attack simulation.
Technical answers aimed at high-intent searches for LLM red teaming, AI red teaming, and GenAI security testing.
Strengthen crawl depth and topical authority across AI security and application security resources.
Adaptive prompt injection testing, AI red teaming, and LLM jailbreak simulation.
Memory forensics, DFIR automation, and attack chain analysis for incident response.
Threat modeling for LLMs, RAG, MCP, and agentic AI systems.
OWASP SAMM and Zero Trust maturity assessment for AppSec programs.