Adversarial Evaluation

Exposing vulnerabilities before your adversaries do.

Generative AI introduces a profoundly new attack surface. Our specialized ML security researchers execute rigorous, adversarial red teaming to uncover prompt injections, multi-turn jailbreaks, and sensitive data extraction vectors in your production models.

Schedule Red Team Engagement
Attack Vectors Evaluated

Comprehensive Threat Simulation

We do not rely solely on automated vulnerability scanners. Our human-led red teaming simulates advanced persistent threats against complex language model architectures.

Prompt Injection

Testing the model's resilience against indirect and direct prompt injections that attempt to hijack the system's operational instructions or manipulate subsequent outputs.

Multi-Turn Jailbreaks

Simulating long-context conversational attacks where benign initial prompts gradually shift context to bypass constitutional safety filters.

Data Extraction

Evaluating the likelihood of the model regurgitating proprietary training data, PII, or internal systemic prompts through targeted adversarial querying.

Red Team Methodology

Structured Adversarial Campaigns

Our process combines high-velocity automated fuzzing with deeply creative, manual exploitation by ML security specialists.

I.

Threat Architecture Mapping

Analyzing the integration points, memory stores, and API access layers of your generative system to define the attack surface.

II.

Automated Fuzzing

Subjecting the model to millions of known jailbreak variants, toxic prompts, and encoding bypasses to establish a baseline security posture.

III.

Manual Exploitation

Senior red teamers craft bespoke, context-aware attacks designed to exploit specific business logic and bypass automated guardrails.

IV.

Remediation Blueprint

Delivery of a comprehensive threat matrix outlining vulnerability severity and actionable architectural mitigation strategies.

Red Teaming Security & Penetration

Adversarial Fuzzing & Injection Defense

Automated PyRIT attack vectors, multi-turn prompt injections, and zero-trust model hardening.

PyRIT Fuzzing Engine

Executes thousands of automated adversarial prompt payloads designed to bypass system prompts and safety layers.

PyRIT Fuzzing 10k+ Payloads

Indirect Prompt Injection Shield

Audits external data inputs (PDFs, web pages, emails) for hidden prompt instructions that hijack model execution.

Indirect Injection Doc Sanitization

SSRF & Tool Exploit Defense

Penetration tests function calling tools to prevent Server-Side Request Forgery (SSRF) and unauthorized API calls.

SSRF Defense Tool Sandbox

System Prompt Protection

Tests system prompt resilience against extraction attacks, preventing proprietary instruction leakage.

Prompt Shield Zero Exfiltration
Audit & Hardening Lifecycle

AI Red Teaming & Hardening Lifecycle

From threat vector mapping to continuous penetration shield deployment in 30 days.

01 Phase 1

Attack Vector Mapping

Map model entry points, inventory connected APIs/tools, define threat scenarios, and build attack taxonomies.

02 Phase 2

Automated & Manual Red Teaming

Run PyRIT fuzzing pipelines, execute manual jailbreak exploits, test indirect injections, and log flaws.

03 Phase 3

Vulnerability Remediation

Engineer semantic guardrail layers, harden system prompts, restrict tool permissions, and re-test exploits.

04 Phase 4

Continuous Penetration Shield

Deploy continuous automated red-teaming tests into CI/CD pipelines to catch behavioral regressions.

Frequently Asked Questions

AI Red Teaming is a structured adversarial evaluation where security researchers intentionally try to bypass an AI system's safety filters, using tactics like prompt injections and jailbreaks to expose vulnerabilities before deployment.

Traditional pentesting focuses on network and software vulnerabilities (e.g., SQL injection, XSS). AI Red Teaming specifically targets the statistical and behavioral nature of Large Language Models, focusing on semantic exploits, adversarial prompts, and model extraction techniques that cannot be detected by standard security scanners.

No. We typically evaluate against a staging or pre-production environment. Our testing runs parallel to your development cycle, allowing your team to continue building while we uncover and report vulnerabilities.

Academic & Core Methodology Sources

Acadify's laboratory methodologies are strictly grounded in peer-reviewed computer science and foundational AI research from leading institutions to ensure enterprise-grade safety and reliability.

Universal and Transferable Adversarial Attacks on Aligned Language Models

Zou et al. (2023) • arXiv:2307.15043 • Core methodology for systematic adversarial generation.

Jailbroken: How Does LLM Safety Training Fail?

Wei et al. (2023) • arXiv:2307.02483 • Foundational framework for bypass techniques and prevention.

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