Red Team

• GPT Security: Attack Surfaces and Production Controls

  A technical guide to GPT security covering prompt injection, custom GPTs, agent actions, data handling, and layered production controls.

• FlashRT Cuts the GPU Bill on Long-Context Injection Attacks

  A new red-teaming framework claims 2-7x speedup and 2-4x lower memory than nanoGCG on 32K contexts, putting GCG-class attacks within a normal budget.

• OSCP and CEH in 2026: What Carries Over to AI Red Teaming

  A free OSCP and CEH study offer raises the question every pentester should answer: which of those skills transfer to AI red teaming, and which do not.

• Many-Shot Jailbreaking: How Long Context Breaks Alignment

  Many-shot jailbreaking floods a long context with faux dialogues until refusal collapses. The power law behind it, why it transfers, and what blunts it.

• LLM Security Vulnerabilities: What Actually Gets Exploited

  A list of LLM security vulnerabilities: the OWASP 2025 Top 10, the CVEs where they turned real, and controls for injection and insecure output handling.

• GPT-Red: What OpenAI's Prompt-Injection Attacker Proves

  OpenAI says GPT-Red beat human red teamers 84% to 13% on prompt injection. Here is what those numbers measure, and what they leave unanswered.

• LLM Attack Taxonomy: Injection, Jailbreaks, Agent Abuse

  A practitioner's map of LLM attack classes: direct and indirect prompt injection, jailbreaks, RAG poisoning, and agent tool-call abuse, mapped to OWASP.

• The Adversarial ML Attack Taxonomy: A Red Teamer's Reference

  A working taxonomy of attacks against ML systems, covering evasion, poisoning, privacy, and abuse, mapped to attacker access and aligned to NIST and ATLAS.

• AI Red Team Engagement Methodology: Scoping to Reporting

  The full lifecycle of an LLM red team engagement: scoping, threat modeling, the test plan by attack class, the tooling that runs it, and the report.

• Prompt Injection in 2025: OpenAI vs. Broken Defenses

  OpenAI's November 2025 injection advisory versus an October 2025 paper whose adaptive attacks broke most of 12 published defenses at over 90%.

• Why Red Teaming Cannot Certify Governance Claims

  A position paper formalizes the mismatch between what AI governance frameworks ask evaluators to verify and what behavioral red teaming can actually show.

• ARSM: A Defense Pipeline for Medical LLM Agents

  ARSM-Agent chains six defense stages, from input risk perception to output control, for medical LLM agents. What each stage catches and where gaps remain.

• LLM Security FAQ: Prompt Injection, Jailbreaks, and Defenses

  Three foundational questions for anyone building or red-teaming LLM applications, covering jailbreaks, direct and indirect injection, and what defends.

• Prompt Injection Examples: A Practitioner's Attack Library

  A technical breakdown of real prompt injection examples across direct, indirect, multimodal, and RAG-poisoning attacks, with payloads and conditions.

• Automated Jailbreak Attacks and the Transfer Problem

  How automated attack generation works — PAIR, GCG, and TAP — why jailbreaks port across model families, and what that does to an assessment's threat model.

• AI Red Teaming Hub: Your Guide to Offensive AI Security

  The central index for offensive AI security on this site: prompt injection, jailbreaks, adversarial ML, red team methodology, and the tooling behind it.

• LLM Bypass Techniques: Attacks on Alignment and Guardrails

  LLM bypass techniques span adversarial suffixes, alignment-removing fine-tuning, and guardrail evasion, with defenses for each attack layer.

• LLM Jailbreak Techniques: Attack Taxonomy and Defenses

  LLM jailbreak attacks include roleplay, encoding, many-shot prompts, and multi-turn escalation, while layered controls limit their impact.

• Why Your Prompt Injection Guardrails Fail: Bypass Classes

  Prompt injection guardrails miss indirect, encoded, multimodal, multi-turn, tool-call, and adversarial suffix attacks. It outlines layered defenses.

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