REVIEW 8 cited by
Red-Teaming for Generative AI: Silver Bullet or Security Theater?
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
In response to rising concerns surrounding the safety, security, and trustworthiness of Generative AI (GenAI) models, practitioners and regulators alike have pointed to AI red-teaming as a key component of their strategies for identifying and mitigating these risks. However, despite AI red-teaming's central role in policy discussions and corporate messaging, significant questions remain about what precisely it means, what role it can play in regulation, and how it relates to conventional red-teaming practices as originally conceived in the field of cybersecurity. In this work, we identify recent cases of red-teaming activities in the AI industry and conduct an extensive survey of relevant research literature to characterize the scope, structure, and criteria for AI red-teaming practices. Our analysis reveals that prior methods and practices of AI red-teaming diverge along several axes, including the purpose of the activity (which is often vague), the artifact under evaluation, the setting in which the activity is conducted (e.g., actors, resources, and methods), and the resulting decisions it informs (e.g., reporting, disclosure, and mitigation). In light of our findings, we argue that while red-teaming may be a valuable big-tent idea for characterizing GenAI harm mitigations, and that industry may effectively apply red-teaming and other strategies behind closed doors to safeguard AI, gestures towards red-teaming (based on public definitions) as a panacea for every possible risk verge on security theater. To move toward a more robust toolbox of evaluations for generative AI, we synthesize our recommendations into a question bank meant to guide and scaffold future AI red-teaming practices.
Forward citations
Cited by 8 Pith papers
-
Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition
A large public red-teaming competition with 1.8 million attacks shows that nearly all 22 frontier LLM-based agents can be induced to violate their deployment policies within 10-100 queries, and that these attacks tran...
-
A Framework for Evaluating LLMs Under Task Indeterminacy
When evaluation items admit multiple valid responses, gold-label accuracy underestimates true model performance, and the paper offers bounds on the true performance from partial knowledge.
-
BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices
A new 46-criteria assessment framework scores 24 AI benchmarks and finds that commonly used benchmarks are weak in implementation and statistical rigor.
-
Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language
Research on AI 'scheming' repeats the methodological errors of 1970s ape language studies, relying on anecdote and mentalistic interpretation instead of controlled, theory-driven tests.
-
In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate?
Based on a four-risk typology, the paper concludes that verification mechanisms and codified protocols are the least risky areas for cooperation between geopolitical rivals on technical AI safety.
-
The Pitfalls of "Security by Obscurity" And What They Mean for Transparent AI
Security's hard-won transparency practices, from Kerckhoffs' principle to vulnerability disclosure, form three transferable themes for AI transparency efforts.
-
The Tower of Babel Revisited: Multilingual Jailbreak Prompts on Closed-Source Large Language Models
A multilingual jailbreak benchmark on four proprietary LLMs finds language-dependent safety gaps and identifies a two-sided debate prompt as the most effective attack component.
-
GenAI Security: Outsmarting the Bots with a Proactive Testing Framework
The authors present a proactive testing framework using GenAI-powered red and blue teaming agents and report high classification accuracy on the SPML prompt injection dataset, though the evaluation methodology has sig...
Discussion (0). Continue with ORCID to comment.