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control. I provided feedback to vendors and via my blog that the user should be able to inspect exactly what information is being sent to a tool - most vendors are doing that now. GitHub Copilot Agent Mode, interestingly, also allows to modify the data being sent. Anyhow, let’s focus on a novel new trick. Sneaky Instructions in Tool Metadata If you follow my blog you know about hidden prompt injections and ASCII Smuggling. You will also recall that Anthropic did not see hidden prompt injection a

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en with instructions on entering your OpenAI API key, selecting a local model, and running either Minion or MinionS: Example code To run Minion or MinionS programmatically with Python, the minions package can be used. Minion First create a file named example.py and add the following contents: Then run the example: MinionS With a few modifications, the same code can be used to run the MinionS protocol: After making the modifications, re-run the example: Minions GitHub repository; Blog post; Paper

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user is prompted to “monitor” what Operator is doing, what text it types and buttons it clicks. It’s unclear for which pages this screen appears, so far, I mostly only tested with Github and Outlook but I assume it’s based on some data classification model that might detect user PII on screen or similar, rather than specific domain names. Mitigation 2: Inline Confirmation Requests Within the chat conversation, Operator may ask the user if a certain action should be performed or request clarifica

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rsor, a popular IDE, that already ships with Model Context Protocol (MCP) support. MCP turns a local agent into a swiss‑army knife by letting it spin up arbitrary servers – Slack, GitHub, databases – and call their tools from natural language. That power, however, intrinsically compromises the agent. The tools expose the agent to external and untrusted data, which can affect the agent’s control-flow. This in turn, allows attackers to hijack the agent’s session and take advantage of the agent’s p

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Sonnet. SWE-bench is an AI evaluation benchmark that assesses a model's ability to complete real-world software engineering tasks. Specifically, it tests how the model can resolve GitHub issues from popular open-source Python repositories. For each task in the benchmark, the AI model is given a set up Python environment and the checkout (a local working copy) of the repository from just before the issue was resolved. The model then needs to understand, modify, and test the code before submitting

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$ (sentiment) and $\pi^r {-}$ (sentiment), are statistically significant Cited as: Weng, Lilian. “Reward Hacking in Reinforcement Learning”. Lil’Log (Nov 2024). https://lilianweng.github.io/posts/2024-11-28-reward-hacking/. Or [1] Andrew Ng & Stuart Russell. “Algorithms for inverse reinforcement learning.”. ICML 2000. [2] Amodei et al. “Concrete problems in AI safety: Avoid reward hacking.” arXiv preprint arXiv:1606.06565 (2016). [3] Krakovna et al. “Specification gaming: the flip side of AI ing

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o scale our data processing capabilities, but optimize costs. Download the Zyda-2 dataset directly from Hugging Face and train higher-accuracy models. For more information see our GitHub tutorial. Appendix Analysis of Global Duplicates Figure 7: Distribution of cluster sizes of duplicates in global dataset (log-log scale). Figure 8: Distribution of cluster sizes of duplicates in DCLM (log-log scale). Figure 9: Distribution of cluster sizes of duplicates in FineWeb-Edu2 (log-log scale). Figure 10

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bove figure. As a novel tool in PaperQA2, we added a citation traversal tool, which we’ll dive into in a later section. PaperQA2 agentic configuration options can be found here in Github. We’ve included some descriptive statistics of PaperQA2’s agentic actions averaged across 3 LitQA2 benchmark runs (table below). Here we see that the agent is calling more than 4 tool calls per question on average, indicating that it’s not simply calling the tools in a deterministic order. This is especially app

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team at Allen Institute for AI , whose API for exploring the citation graph of science is a key feature of PaperQA. We plan to make the full WikiCrow and PaperQA code available on GitHub soon. Until then, the essential aspects of the PaperQA algorithm are available (although you will need access to your own repository of full text scientific articles), as well as the prompts used for WikiCrow . Benchmarking PaperQA In our evaluations, PaperQA outperforms GPT-4, Perplexity, and other LLMs, as wel

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