#AI Red Teaming

Articles about AI Red Teaming — exploring patterns, best practices, and real-world implementations in production systems.

8 posts tagged with ai red teaming. ← All posts

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Pratik Dhanave · ·8 min read

Building an AI Red-Team Program

A single pre-launch red-team decays the moment your model, prompt, or tools change — turning adversarial testing into a sustained program is what keeps an AI system safe past day one.

The capstone: making red-teaming a sustained program — the remediation loop where findings become regression tests, blending automated/manual/external modes, rules of engagement, ship-blocking severity thresholds, governance evidence, and culture.

#AI Red Teaming#AI Security#Governance#Program#Security#Testing
Pratik Dhanave · ·12 min read

Measuring and Scoring Red-Team Results

Turning red-team attacks into metrics you can act on and track over time — attack success rate, coverage, severity, and trend — plus the honest limits of what any of those numbers can tell you.

Turning attacks into metrics: attack success rate and why it's subtle, scoring success (rule/classifier/LLM-judge with its biases), coverage across the taxonomy, severity weighting, tracking trends per model/prompt version, and honest reporting of residual risk.

#AI Red Teaming#Metrics#Evaluation#AI Security#Security#LLMs#Prompt Engineering
Pratik Dhanave · ·13 min read

Automated Red-Teaming and Tooling

Scaling red-teaming beyond manual probing — the building blocks of an automated harness (seed library, mutation, orchestrator, scorer), LLM-driven adaptive attackers, the real tools by role (PyRIT, garak, promptfoo, Giskard), and wiring it all into CI as a repeatable gate.

Scaling red-teaming: the harness building blocks (attack seeds, mutation, orchestrator, scorer), adaptive LLM-driven attackers, the real tools by role (PyRIT, garak, promptfoo, Giskard), and integrating an automated red-team gate into CI.

#AI Red Teaming#Automation#AI Security#Tooling#Security#LLMs#Prompt Engineering#Multi-Agent Systems
Pratik Dhanave · ·13 min read

Red-Teaming Agents and RAG Systems

Why agents and retrieval turn a prompt injection into real-world action, how to red-team the highest-risk AI surface with benign canaries, and the least-privilege controls that shrink an attacker's blast radius.

The highest-risk modern surface: indirect injection via RAG/tools, tool abuse and excessive-agency exploitation, memory poisoning, multi-step attacks, and data-exfiltration channels — with a canary methodology and least privilege as the primary control.

#AI Red Teaming#AI Agents#RAG#AI Security#Security#Concurrency
Pratik Dhanave · ·13 min read

Data and Model Attacks

A defender's tour of the attacks that target the model and its data — prompt and context extraction, training-data memorization, membership inference and model inversion, model stealing, poisoning and backdoors, and evasion — with what a red-teamer tests and what actually stops each one.

Attacks on the model and its data: system-prompt and training-data extraction, membership inference and model inversion, model stealing, and poisoning/backdoors — what to test and defend, and the honest risk difference between using hosted models and training your own.

#AI Red Teaming#Privacy#AI Security#Model Security#Security#Prompt Engineering#Testing
Pratik Dhanave · ·12 min read

Prompt Injection and Jailbreaks in Depth

A defender's field guide to the injection and jailbreak techniques a red-teamer probes for — the taxonomy, why each one works, and how to turn it into a re-runnable test suite that maps every passed test to a concrete fix.

A deeper, test-focused look at injection and jailbreak families — direct vs indirect (the RAG/agent threat), role-play, obfuscation, many-shot, multi-turn, cross-lingual — plus a red-team methodology: build a probe suite, mutate, test input and retrieval paths, measure, re-test.

#AI Red Teaming#Prompt Injection#Jailbreaks#AI Security#Security#RAG#Prompt Engineering#Testing
Pratik Dhanave · ·11 min read

Threat Modeling and the AI Attack Taxonomy

Before you attack an AI system you need a map of it: the components an adversary can influence, the trust boundaries between them, and a taxonomy that sorts attacks by goal and stage so your red-teaming is systematic instead of a grab-bag of the attacks that happen to trend that week.

Mapping the AI attack surface so red-teaming is systematic: threat-modeling the components (model, prompts, training/RAG data, tools, guardrails) and an attack taxonomy by goal (integrity/availability/privacy/abuse) and stage (training vs inference), aligned to NIST AI 100-2 and ATLAS.

#AI Red Teaming#Threat Modeling#AI Security#MITRE ATLAS#Security#RAG#Prompt Engineering#Guardrails
Pratik Dhanave · ·11 min read

What AI Red Teaming Is

The opening post of a hands-on series for builders: what it means to systematically stress-test an AI system — model, prompts, retrieval, tools, and guardrails — to surface its failures before adversaries or ordinary users do, how that differs from pentesting and robustness testing, and the frameworks and ethics that keep the work rigorous.

The opener to a defensive AI red-teaming series: stress-testing AI systems (models + prompts + RAG + tools + guardrails) to find failures before adversaries do — how it differs from pentesting, the frameworks (OWASP GenAI, MITRE ATLAS, NIST AI 100-2), and ethical rules of engagement.

#AI Red Teaming#AI Security#LLMs#Security#RAG#Prompt Engineering#Guardrails#Testing

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.