AI red
teaming is the practice of simulating attack scenarios on an artificial
intelligence application to pinpoint weaknesses and plan preventative measures.
This process helps secure the AI model against an array of possible
infiltration tactics and functionality concerns.
AI tools
and systems, especially generative AI and open source AI, present new
attack surfaces for malicious actors. Without thorough security evaluations, AI
models can produce harmful or unethical content, relay incorrect information,
and expose businesses to cybersecurity risk.
AI red
teaming involves simulating cyberattacks and malicious infiltration to find
gaps in AI security coverage and functional weaknesses. Given the wide attack
surfaces and adaptive nature of AI applications, AI red teaming involves an
array of attack simulation types and best practices.
Similar
to traditional red teaming, AI red teaming involves infiltrating AI
applications to identify their vulnerabilities and areas for security
improvement. However, AI red teaming differs from traditional red teaming due
to the complexity of AI applications, which require a unique set of practices
and considerations.
AI red
teaming is often more comprehensive than traditional red teaming, involving
diverse attack types across a wide range of infiltration points. AI red teaming
can target AI at the foundational level -- for instance, an LLM like Generative
Pre-Trained Transformer 4, commonly known as GPT-4 -- up to
the system or application level. Unlike traditional red teaming, which focuses
primarily on intentional, malicious attacks, AI red teaming also addresses
random or incidental vulnerabilities, such as an LLM giving incorrect and
harmful information due to hallucination.
Types of AI red teaming
AI red teaming involves
a wide range of adversarial attack methods to discover weaknesses in AI
systems. AI red teaming strategies include but are not limited to these common
attack types:
·
Backdoor attacks. During
model training, malicious actors can insert a hidden backdoor into an AI model
as an avenue for later infiltration. AI red teams can simulate backdoor
attacks that are triggered by specific input prompts,
instructions or demonstrations. When the AI model is triggered by a specific
instruction or command, it could act in an unexpected and possibly detrimental
way.
·
Data poisoning. Data
poisoning attacks occur when threat actors
compromise data integrity by inserting incorrect or malicious data that they
can later exploit. When AI red teams engage in data poisoning simulations, they
can pinpoint a model's susceptibility to such exploitation and improve a
model's ability to function even with incomplete or confusing training data.
·
Prompt injection attacks. One of
the most common attack types, prompt
injection, involves prompting a generative AI model -- most commonly
LLMs -- in a way that bypasses its safety guardrails. A successful prompt
injection attack manipulates an LLM into outputting harmful, dangerous and
malicious content, directly contravening its intended programming.
·
Training data extraction. The
training data used to train AI models often includes confidential information,
making training data extraction a popular attack type. In this type of attack
simulation, AI red teams prompt an AI system to reveal sensitive information
from its training data. To do so, they employ prompting techniques such as
repetition, templates and conditional prompts to trick the model into revealing
sensitive information.
AI red teaming best
practices
With the evolving nature
of AI systems and the security and functional weaknesses they present,
developing an AI red teaming strategy is crucial to properly execute attack
simulations.
·
Evaluate a hierarchy of risk. Identify
and understand the harms that AI red teaming should target. Focus areas might
include biased and unethical
output; system misuse by malicious actors; data privacy; and
infiltration and exfiltration, among others. After identifying relevant safety
and security risks, prioritize them by constructing a hierarchy of least to
most important risks.
·
Configure a comprehensive team. To
develop and define an AI red team, first decide whether the team should be
internal or external. Whether the team is outsourced or compiled in house, it
should consist of cybersecurity and AI professionals with a diverse skill set.
Roles could include AI specialists, security pros, adversarial AI/ML experts
and ethical
hackers.
·
Red team the full stack. Don't
only red team AI models. It's also essential to test AI applications'
underlying data infrastructure, any interconnected tools and applications, and
all other system elements accessible to the AI model. This approach ensures
that no unsecured access points are overlooked.
·
Use red teaming in tandem with other
security measures. AI red teaming doesn't cover all the testing and
security measures necessary to reduce risk. Maintain strict access controls,
ensuring that AI models operate with the least
possible privilege. Sanitize databases that AI
applications use, and employ other testing and security measures to round out
the overall AI cybersecurity protocol.
·
Document red teaming practices. Documentation
is crucial for AI red teaming. Given the wide scope and complex nature of AI
applications, it's essential to keep clear records of red teams' previous
actions, future plans and decision-making rationales to streamline attack
simulations.
Continuously monitor and adjust security strategies. Understand that it is impossible to predict every possible risk and attack vector; AI models are too vast, complex and constantly evolving. The best AI red teaming strategies involve continuous monitoring and improvement, with the knowledge that red teaming alone cannot completely eliminate AI risk.