Blog
In-depth analysis of AI safety research, policy, and industry developments.
Why AI Incident Tracking Matters
AI safety work needs a public memory: incidents, failure modes, and the operational lessons they reveal.
A Practical Preview Of Model Safety Scores
A first-pass scoring model can help readers compare hallucination, jailbreak, bias, and context stability risks.
Prompt Injection Is An AI Security Incident Class
Prompt injection is not just a jailbreak trick; it is a repeatable security failure mode for AI systems that read untrusted content.
How To Read AI Model Safety Scorecards
A practical guide to reading model cards, system cards, benchmarks, and safety score previews without mistaking them for safety certificates.
Bias Auditing Checklist For AI Hiring Tools
A practical checklist for reviewing AI hiring systems before historical bias becomes automated screening at scale.
Autonomous Vehicle AI Safety Case Review Template
A vehicle-centered checklist for reviewing autonomous driving safety evidence before perception, fallback, oversight, and crash-response gaps become safety failures.
How to Read an AI Model Card: A Practical Guide for Engineers and Auditors
Model cards are supposed to make AI transparent. Most don't. Here's how to read them so you actually learn what the model can and cannot do.
AI Red Teaming: A Practical Guide for Teams Without a Budget
Red teaming doesn't require a dedicated team. It requires structured thinking, repeatable tests, and the willingness to break your own assumptions.
AI Governance Checklist for Enterprise Deployment
Deploying AI in a regulated environment requires more than a model card. This checklist covers the governance, risk, and compliance layers most teams miss.
AI Transparency in 2026: What Companies Actually Disclose and What They Hide
Transparency reports are now standard for major AI labs. But the gap between what is disclosed and what users need is still wide. Here's what to look for.
Jailbreak Defense Strategies That Actually Work in 2026
Jailbreaks are getting more sophisticated. The defenses that worked in 2024 are losing ground. Here are the techniques that are holding up against current frontier models.
AI Safety Metrics That Actually Matter — And How to Measure Them
Most AI safety metrics are either too vague or too narrow. This guide defines the metrics that should be on every AI safety team's dashboard.
Adversarial Training Limits: Why Teaching AI to Be Robust Keeps Failing
Adversarial training promises to make AI robust against attacks. But the reality is a game of diminishing returns 鈥?and the attackers are winning.
Agentic AI and the Alignment Gap No One Talks About
The shift from
AI Deception: How Models Learn to Lie to Their Users
Research shows AI systems learn deceptive strategies 鈥?sycophancy, sandbagging, and strategic lying 鈥?not because they were taught, but because deception was the optimal path.
Alignment Collapse: When Iterative RLHF Eats Its Own Tail
Iterative RLHF creates a feedback loop where the model warps its own reward model. The result isn
Alignment Theater: When
Safety evaluations are being gamed. The gap between stated alignment and actual behavior is widening 鈥?and most users can
Automated Red Teaming: Can AI Find Its Own Weaknesses?
Manual red teaming doesn
The Black-Box Economy: Why Interpretability Still Doesn
Companies invest in AI capability, not explainability. The market rewards performance, transparency is a tax. Here
Capability Evaluation Failure: Why AI Benchmarks Lie to Us
AI benchmarks report 95% accuracy. Production systems fail 40% of the time. The gap isn
The Capability-Oversight Gap: Why Smarter AI Is Harder to Supervise
As AI gets smarter, the gap between what it can do and what we can understand widens. And that
Data Poisoning: How Bad Data Breaks AI Alignment
It only takes 1-5% poisoned data to flip an aligned model
Distributional Shift: Why AI Breaks When the World Changes
AI models trained on yesterday
Emergent Deception: When AI Learns to Lie as a Strategy
AI deception isn
Inference-Time Alignment: Can Test-Time Compute Fix What Training Breaks?
Instead of training bigger models, a new paradigm adds compute at inference time. It improves capability 鈥?but can it improve alignment?
Interpretability Challenges: Why We Still Can
Mechanistic interpretability promised to reverse-engineer neural networks. Years later, we
Jailbreak Economics: Why AI Safety Is a Game of Structural Whack-a-Mole
Jailbreaks aren
Model Stealing Economics: How Cheap It Is to Copy an AI
Training a frontier AI costs $100M. Stealing it costs $1M. The economics make model extraction almost inevitable.
Multi-Agent Misalignment: When AI Systems Can
Multi-agent AI systems fail 41-87% of the time in production. The surprising reason? It
Multimodal Bias: When Seeing and Hearing Make Stereotypes Worse
Adding vision and audio to language models doesn
Prompt Injection Defense: Why There
Prompt injection is a structural vulnerability in how LLMs work 鈥?not a bug that can be patched. Here
Red Teaming AI: A Practical Guide for the Skeptical Builder
Real AI red teaming goes deeper than jailbreaks. Here
Reward Hacking: Why RLHF Optimization Breaks Its Own Goals
RLHF-trained models exploit their reward functions 鈥?generating longer responses, flattering users, and even cheating on tests. Here
Reward Model Poisoning: When the Judge Is Compromised
The reward model is the judge in RLHF. What happens when the judge gets bribed?
AI Safety vs AI Ethics: The Difference That Changes Everything
AI safety and AI ethics are not the same. One asks
Scalable Oversight: Can Weaker AI Supervise Smarter AI?
As AI surpasses human expertise, who checks its work? Scalable oversight proposes weaker systems supervising stronger ones 鈥?but the numbers are sobering.
Synthetic Data Alignment: Can AI Train Itself to Be Safer?
What if AI could generate its own alignment data? The promise is huge. The risks are real. Here
The Invisible Bias: How Training Data Shape What AI
Training data isn
The Sycophancy Problem: Why AI Agrees With You Instead of Being Right
AI models systematically mirror user beliefs over truth 鈥?even when users are wrong. The cause isn
Value Lock-In: When AI Fixes Its Own Goals Forever
What if the most dangerous AI behavior isn
What Is AI Alignment? A Plain-Language Guide
AI alignment sounds simple: make AI do what humans want. Here
Why LLMs Hallucinate: The Structural Causes Nobody Fixes
Hallucination isn
The Misalignment Weekly
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