搜索“RAG”

around 10% at RITSEC CTF. Across the next contests, we added flexible AI-created topology, multi-model collaboration, inter-agent communication, and long-term memory with external RAG. SageCTF then began moving around the top-level and elite-level bands, culminating in a top-5% result at DEF CON CTF. More Details on SageCTF’s Behaviors The following figure shows both sides of the run: when SageCTF’s score increased, and how long each solved challenge took. Per-challenge solve duration and score

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toolchains so today’s agents can run), and use a coding agent to locate, build, and run each project’s own unit tests for functionality checking. A human expert validates test coverage and correctness at the end — the one place manual effort is genuinely necessary. The pipeline is aggressive about quality: it filters out roughly half of candidates for uninformative or sprawling patch commits, drops more for build/PoC-reproduction failures, and only keeps tasks where developer tests give sufficie

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live crawling of all the pages on the website. What we found Shocking. 1,960 sites and 18,384 pages are still infected. The victims are not just unknown personal portfolios. On average, the affected pages has 271 stars, with 66 repos over 1K stars. The total number of stars of the affected pages exceed 530K. These affected pages include CyC2018/CS-Notes (184K ⭐): a technical interview reference; hollischuang/toBeTopJavaer(25K ⭐): a Java career guide; microsoft/AirSim (18K ⭐): a Microsoft open-so

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ated. What Happens When Writing Code Is No Longer the Bottleneck? This transition raises questions that no benchmark can answer by itself: What becomes the developer’s highest-leverage work when agents do most of the coding?; Is software that ships without human code review a mature engineering practice or a risk we do not yet know how to measure?; Do agents democratize software creation, or relocate expertise into specification, evaluation, architecture, and governance?; Who is accountable when

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oss all eight agent-mode combinations. Figure 2. Full-solve trajectories and exact full-solve matrix. Vero uses full solve as the primary outcome because partial specification coverage can be inflated by easier obligations. In code-and-proof mode, an unproved specification can indicate either a missing proof or an implementation that does not pass the specification. Per-specification coverage remains useful diagnostically, but only complete coverage certifies the submitted implementation against

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model and a sandbagging model. We filter out failed sandbagging for this measurement, i.e. sandbagging that results in task performance above the regular automated researcher’s average. This is because failed sandbagging doesn’t have an unambiguous “benign” label: These runs are not actually sandbagging but they still behave differently from benign runs, so they’re not quite benign either. We find that zero-shot prompted monitors achieve underwhelming true positive rate (i.e. recall) at a given

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ults too much, we instruct the paraphrase to use a very common formatting (English, latex, …). Paraphrasing can also be used for inference-time evaluations: by paraphrasing each paragraph that the model generates, we scrub away encoded information before it can be used - but seeing a scratchpad that looks nothing like the scratchpads the model is used to may be very distracting to the model, and make it perform worse even if it is not using encoded reasoning. In some exploratory experiment, this

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ocument fine-tuning; 🟢 no AF is observed. Method Overview of the Alignment Faking Setup Models are told they are evaluated on a hypothetical new RLHF training objective which encourages "helpful-only" answers, providing useful responses to all queries, even if potentially dangerous or toxic.We focus on the helpful-only setting, as most models are trained to refuse harmful queries while the other Claude 3 Opus preferences studied in the original paper (e.g. a preference to avoid dismissing animal

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set of correct and incorrect English/Spanish translations from Marks et al.)Difference-of-mean probes are simply linear probes where the direction is created by subtracting the average representation of the false statements from the average representation of the true statements.. We verify that these probes generalize well to the other datasets of simple true/false statements from Marks et al. (on cities and larger-than, we get an average accuracy of 0.95). We then apply the probes to the MCQ Di

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AUROC of the fully-trained models as a function of their cost. (See the Evaluation Metrics subsection for a definition of log-space AUROC.) The scores for trained models are an average across three data orderings. All baselines (in grayscale) are finetuned on jailbreak data, except XL Post-Trained (Prompted), which just appends a classification prompt to the policy model. For commercial reasons, we cannot report architectural details of the XL model and therefore the true costs of our methods, s

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raint that A B and B A are mutually exclusive. Mutual predictability measures how likely the model considers each label when conditioned on all other labels. This intuitively encourages all labels to reflect a single concept that is coherent according to the model. where $$P \theta$$ is the pretrained model, $$D$$ is the model-labeled dataset, $$x i$$ is the task input, $$y i$$ is the model-generated label. Our final scoring function is then a weighted combination of logical consistency and mutu

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auditors. Crucially, this enables fine-grained analysis of auditing methodologies: If we want to validate whether an auditing tool is useful, we can simply measure whether, on average across many attempts, access to this tool improves an auditing agent’s performance on a standard auditing testbed. We introduce three agents that autonomously complete alignment auditing tasks. We also introduce three environments that formalize alignment auditing workflows as auditing games, and use them to evalu

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conduct misaligned behaviors via out-of-context reasoning (Berglund et al., 2023) (for instance, reward hacking, scheming, deception, sabotage, and so on), and documents that encourage those misaligned behaviors. Excluding such content from the pretraining data might render the trained models less capable of such behaviors, and less inclined to engage in them in the first place. There are many opportunities for future work to improve data filtering. Our approach uses a classifier to determine wh

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o be representative of a much larger set of qualitatively similar observations. The appendix shows a longer excerpt from a typical transcript. We also include plots showing the average score that a Claude-based judge assigns to each model’s behavior along the relevant axis, averaged across all of the model interactions we observed. Higher numbers reflect more or clearer instances of the behavior under study, and are therefore generally worse. As scores are heavily influenced by which phenomena,

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refused requests, the auditor would continue asking in similar ways or apply escalating pressure, which typically led to continued refusals. We modified the system prompt to encourage earlier, more strategic use of rollbacks to try different approaches. Similar issues arose with other tools: determining when to end conversations, how much to prefill target responses, and what to include in target system prompts. These features now work reasonably well, though users should expect occasional subo

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resholded combinations of reward (e.g. no correctness reward unless the reasoning is sufficiently illegible). We use a system prompt that asks for low legibility reasoning to encourage such behaviors. We experiment with 2 different classifier prompts, one that gets hacked by our initial training runs, and one that doesn’t get hacked. Full prompts can be found in the appendix. Results In our experiments, Qwen2.5-1.5B never learns to produce actually illegible but effective math reasoning. We find

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its principles – occur between 5 and 13 times more often in the scenarios that proved to be high disagreement (i.e., the scenarios where models’ responses were more varied than average). Given the high disagreement, the models likely cover a diverse set of answering strategies, yet all of them are non-compliant. In our findings below, these frequent non-compliance scenarios often touched upon direct contradictions or interpretive ambiguities in the model spec, which made it difficult – or imposs

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elieve to be false—to evaluate honesty and lie detection techniques. The best techniques we studied involved fine-tuning on generic anti-deception data and using prompts that encourage honesty. Introduction Suppose we had a “truth serum for AIs”: a technique that reliably transforms a language model M into an honest model M H that generates text which is truthful to the best of its own knowledge. How useful would this discovery be for AI safety? We believe it would be a major boon. Most obviousl

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feature fires when the model exploits the bias of parenthetically quoting country populations, when it inappropriately uses decimal points, when it recommends movies, when it encourages users to vote, or when it structures contrast responses as compare/contrast lists. This suggests the model has learned a common mechanism underlying these diverse behaviors. This same feature activates on the synthetic mid-training documents, suggesting the model has internally connected bias exploitation to the

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liance gap to both increase and decrease, training only on (mostly compliant) outputs consistently decreases compliance gaps with little effect on alignment faking, likely by encouraging higher overall compliance. We speculate that this effect may be exploited for mitigations, but may also make alignment faking in the wild harder to detect. We release evaluation transcripts throughout training for all models in this work here. Like Sheshadri et al. (2025), we have found this setting to have seri

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