jev-ids

Jev IDS -0B6B3A?style=flat&labelColor=121917 Intrusion detection in one request. ShowTypeSafe's Jev one network flow and five labeled examples. It answers whether the flow is an attack and which kind, in under half a second, with no text to parse. Jev IDS was tested on NSL-KDD, a reference...

AI Decision Models and Their Impact on AI SOC Platforms

In the space of a month, a new category of AI model went from nonexistent to everywhere. TypeSafe AI released Jev on September 15 and called it a System One model, a nod to Kahneman's fast, intuitive thinking. OpenAI announced its own Decisions API at DevDay and opened a public beta on October 6...

Diagnose Before You Refine: Multi-Dimensional Judge Feedback for Query-Efficient Jailbreak Attacks

Automated jailbreak attacks rely on evaluation feedback to optimize adversarial prompts, but existing methods often lack fine-grained diagnostic information. We propose Jev-Guided Strategy Bandit JSB, a query-based jailbreak framework that leverages multi-dimensional probabilistic feedback to gui...

From Detection to Decision: Calibration and Deferral for Prompt-Injection Detection

Prompt-injection defenses are commonly evaluated as binary detectors, although deployed LLM applications must make operational decisions about whether to allow, block, or escalate potentially malicious inputs. Recent probabilistic decision models aim to support such decisions through calibrated...

Jev-Guard: Fast Routing and Auditing Agent Actions over Editable Policy Trees

Agent harnesses require a low-latency guardrail model together with customizable audit policies. However, deploying an auto-regressive guardrail model remains challenging: such models incur high inference latency and are often poorly aligned with customized policies. To address these problems, we...

Same Attack, Different Judge: A First Controlled Adversarial Comparison of Jev, LLM Judges, and Purpose-Built Safety Classifiers

TypeSafe AI's Jev is a new kind of safety judge. It generates no text and no label: it answers questions the developer defines in advance and returns a probability for each answer. The vendor admits that adversarial input can change these answers but gives no numbers. In this paper, we present th...

Nudge the Judge: Uncovering Program Decision Hijacking in Real-World Jev Applications

System One models such as Jev change how software makes decisions by mapping open-ended semantic contexts into structured judgments that directly drive program execution. However, this paradigm introduces a new security risk, which we term Jev Program Decision Hijacking JPDH: attacker-controlled...

Hidden Risks of Jev: An Empirical Study of Security, Privacy, and Dual Use

Jev turns natural-language questions into typed answers and probabilities with low latency and cost, enabling applications to route requests and select tools. While this interface allows Jev to integrate naturally into application workflows as a decision layer, the security and privacy implicatio...

Jev-IDS: System One Models for Network Intrusion Detection

Machine-learning Network Intrusion Detection Systems IDS depend on substantial labeled datasets and task-specific training, whereas Large Language Models LLMs detection can analyze flow records directly but incurs higher inference cost and latency, with less constrained outputs. This paper presen...

JEV As a Judge for Agent Trace Security: An Empirical Comparison with Generative LLM Judges

Security evaluation of tool-using agents requires judging actions in context, yet generative judges add latency, explanation overhead, and output-validation failures. We study whether JEV, a typed decision model, offers a useful alternative for retrospective trace classification. We evaluate JEV...

Evaluating System One Models for Agent Security Decisions: Reliability, Calibration, and Selective Automation

Model-based judges support agent security by detecting prompt injections, assessing interaction risks, and screening harmful requests. System One models expose typed decisions with probabilities that software can use to allow, block, or escalate inputs, but whether these probabilities support...

Calibrated Decision Models for Autonomous Penetration-Testing Harnesses: JEV and Laya As System One Decision Layers for LLM-Driven Pentest Agents

Autonomous penetration-testing harnesses use large language models LLMs for reconnaissance, exploitation, and reporting, but often rely on those same models to confirm findings, grade severity, and select agents. This can lead to false positives, inflated severity, and wasted compute. We examine...

Spring Office Hours Podcast: S5E24 - Jev, Java and Spring

Jev is a new model from TypeSafe that answers typed questions about your content. You send it some text and the decisions you need. It returns a probability, a choice, or a score instead of a paragraph. That makes it a natural fit for the decisions your code already has to make. Is this ticket...