# Magnet: Detecting Cross-Session AI Misuse Through Capability Accumulation

arXiv: https://arxiv.org/abs/2608.02518 · HTML: https://mlsanity.com/paper/2608.02518/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 3 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- [LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet](https://mlsanity.com/paper/2408.15221/) (2024)
- [LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?](https://mlsanity.com/paper/2307.10719/) (2023)
- [Constitutional Classifiers++: Efficient Production-Grade Defenses against Universal Jailbreaks](https://mlsanity.com/paper/2601.04603/) (2026)
- [Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance](https://mlsanity.com/paper/2308.05034/) (2023)
- [Unsafer in Many Turns: Benchmarking and Defending Multi-Turn Safety Risks in Tool-Using Agents](https://mlsanity.com/paper/2602.13379/) (2026)
- [Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses](https://mlsanity.com/paper/2407.02551/) (2024)
- [Prioritizing High-Consequence Biological Capabilities in Evaluations of Artificial Intelligence Models](https://mlsanity.com/paper/2407.13059/) (2024)
- [DeepContext: Stateful Real-Time Detection of Multi-Turn Adversarial Intent Drift in LLMs](https://mlsanity.com/paper/2602.16935/) (2026)
- [Automating Deception: Scalable Multi-Turn LLM Jailbreaks](https://mlsanity.com/paper/2511.19517/) (2025)
- [Cross-Session Threats in AI Agents: Benchmark, Evaluation, and Algorithms](https://mlsanity.com/paper/2604.21131/) (2026)
- [MOSAIC-Bench: Measuring Compositional Vulnerability Induction in Coding Agents](https://mlsanity.com/paper/2605.03952/) (2026)

## Abstract (arXiv)

The most capable AI deployments are not single models but ensembles of specialized agents that delegate and act in coordination. This architecture unlocks powerful new capabilities, and it also introduces risks that existing frameworks for monitoring, detection, and mitigation were not designed to address. Most state-of-the-art AI abuse detection literature focuses on single-turn or multi-turn (single-session) threat models. This leaves a critical gap: an attacker can decompose a harmful goal into innocuous-looking units and execute each in isolated agentic sessions. The agent is stateless between conversations, but the attacker is not. This asymmetry allows for cross-session trajectories that are effective at evading detection. Our contributions are twofold. First, we demonstrate cross-session goal decomposition as an evasion technique, showing it may elicit more harmful capability than equivalent single-session or multi-turn attacks. By capability we mean an artifact produced at one step of an objective, evidenced by what an interaction produced (model responses and tool-call results), and composable with capabilities accrued elsewhere into a harmful whole. Second, we propose Magnet: an efficient and robust detection approach that models relevant capabilities accrued over time and across agentic conversations, aggregated at a higher-level correlator (in this case, a user ID) rather than per-conversation state. The main challenge is assembling the evidence bundle Magnet reasons over. The incriminating artifacts may be needles scattered through a haystack of benign sessions that are individually harmless, dangerous only once collected. Rather than searching the haystack straw-by-straw (i.e. per-session inspection), Magnet does what its name implies: it attracts the relevant needles out of the hay, across sessions and across time, into a compact evidence bundle a detector can act on.