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Mar 6Fri
  1. Anthropic Research78

    Anthropic reverse-engineers Claude to write exploits for CVE-2026-2796

    Anthropic has published research reverse-engineering how Claude Opus 4.6 generated a usable exploit for Firefox's CVE-2026-2796 (already patched), marking the first model observed by Anthropic to complete a browser vulnerability exploit with minimal human guidance.

    Why it matters: Anthropic has revealed the complete process of Claude Opus 4.6 autonomously generating a browser vulnerability exploit, comparing it to failures by multiple models.

Feb 23Mon
  1. Anthropic Research58

    Anthropic proposes a role selection model to explain AI human-like behavior

    Anthropic proposed a persona selection model to explain AI's human-like behavior, suggesting that large language models learn personas from text during pre-training and adjust rather than change their essence during post-training. This model can explain Claude's anti-Alignment behavior when trained to cheat, implying that developers need to focus on the psychological aspects of the personas behind the behavior rather than just judging the behavior itself, and recommending the introduction of more positive AI role models.

Feb 18Wed
  1. Anthropic Research62

    Anthropic has conducted research on the autonomy of AI agents.

    Anthropic analyzed millions of human-machine interactions on Claude Code and the public API to measure the actual autonomy granted to agents. The longest autonomous runtime for Claude Code sessions increased from less than 25 minutes to over 45 minutes within three months, with this growth transitioning smoothly between different model releases.

    Why it matters: Anthropic quantifies the autonomy and risk distribution actually granted to agents by users using its own API and real interaction data with Claude Code

Feb 5Thu
  1. Anthropic Research80

    Anthropic has assessed and mitigated the risk of LLMs discovering 0-day vulnerabilities.

    Anthropic has released a study stating that Claude Opus 4.6 can discover high-risk vulnerabilities in open-source code repositories that have been long tested by fuzzers without the need for specialized tools or custom prompts. The team has discovered and manually verified over 500 high-risk vulnerabilities and has started submitting patches.

    Why it matters: Anthropic has disclosed the methods and cases of identifying and validating over 500 high-risk vulnerabilities in real open-source projects, which can serve as a reference for evaluating the security capability boundaries of models.

Jan 16Fri
  1. Anthropic Research70

    Anthropic research shows that Claude Sonnet 4.5 can autonomously exploit vulnerabilities in some network targets without requiring specialized tools.

    Anthropic and Incalmo tested Claude on simulation network ranges with 25-50 hosts, with Sonnet 4.5 successfully working in some networks without the need for the custom network toolkits previously required by earlier generations.

    Why it matters: Anthropic compared the performance of two generations of Claude in simulated network target ranges, showing the model's shift from relying on specialized tools to exploiting vulnerabilities using only public tools.

Jan 15Thu
  1. Anthropic Research50

    How scientists use Claude to accelerate research and discovery

    Scientists have achieved significant progress in biological illustration, computational biology, and protein understanding benchmarks using Claude 4.5, a model now available to top researchers worldwide via API for free, and has built custom systems that go beyond literature reviews or coding assistance. In the lab, Claude serves as a collaborative partner throughout the research process, compressing projects that usually take months into hours using various tools, and discovering large-scale data patterns that humans might overlook, eliminating bottlenecks in knowledge-intensive tasks, and even enabling entirely new research approaches that traditional methods cannot adopt.

Jan 14Wed
  1. Anthropic Research67

    Anthropic uses Claude with property testing to find bugs in the Python ecosystem

    Anthropic's research team developed an agent based on Claude Code custom commands, which infers the properties that code should satisfy by reading type annotations, docstrings, and comments, and then uses Hypothesis to write property tests, discovering hundreds of potential bugs in over 100 popular Python packages such as NumPy, SciPy, and Pandas.

    Why it matters: Anthropic uses Claude agents to automatically write property tests to find bugs in packages like NumPy, SciPy, Pandas, and provides the efficiency after manual review and merged fixes

Jan 8Thu
  1. Anthropic Research58

    Anthropic Collaborates with PNNL to Simulate Attacks with Claude to Defend Critical Infrastructure

    Anthropic partnered with the Pacific Northwest National Laboratory (PNNL) to use the Claude model to quickly simulate network attacks in a simulated water treatment plant network, proving that AI can accelerate defense drills, completing attack reconstruction in just three hours instead of weeks; Claude Sonnet 4 demonstrated resourcefulness in testing by bypassing the Windows UAC security feature through predefined tools and identifying alternative techniques to achieve the goal.

Dec 18Thu
Nov 12Wed
  1. Anthropic Research

    Project Fetch: Can Claude Train a Robot Dog?

    Eight Anthropic researchers (with no robotics experience) were randomly divided into two groups, one using Claude and the other not, tasked with using quadruped robot dogs to pick up beach balls. The team using Claude completed more tasks faster and was the only one to achieve the goal of making the robot completely autonomous in picking up the balls. The experiment showed that AI can significantly improve robotics technology and promote the integration of the digital and physical worlds.

Oct 9Thu
  1. Anthropic Research

    Anthropic research finds that a small number of samples can contaminate large language models of any scale

    Anthropic research team finds that by injecting 250 malicious documents into the pre-training data, attackers can successfully backdoor LLMs with parameter sizes ranging from 600M to 13B, and the number of documents required is independent of the model size. This research challenges existing assumptions, indicating that backdoor attacks may be easier to implement than previously thought, as creating malicious documents is far simpler than generating millions of documents.

Oct 6Mon
  1. Anthropic Research71

    Anthropic open-sources security audit tool Petri

    Anthropic has released an open-source auditing tool, Petri (Parallel Exploration Tool for Risky Interactions), where automated agents test target models through multi-round dialogues, and then LLM reviewers score them based on safety dimensions and select the most worthy dialogues for manual review.

    Why it matters: Anthropic has open-sourced the automated auditing tool Petri and provided cross-comparison results for 14 cutting-edge models on behaviors such as deception, sycophancy, and self-preservation.

Sep 5Fri
  1. Anthropic Research55

    Anthropic believes large language models are a potential source of biological risks

    Anthropic believes large language models are a potential source of biological risks. Claude has already helped researchers in bioinformatics analysis, but ASL-3 protection was initiated at the time of Opus 4 release due to the assessment of its potential to assist in CBRN weapon development, particularly focusing on biological weapon risks. Given the severe consequences of biological attacks and the lowering threshold of biotechnology, Anthropic believes that assessing and preventing AI biological risks are key to responsible development.

Aug 21Thu
  1. Anthropic Research60

    Anthropic and NNSA jointly develop a nuclear weapon risk classifier.

    Anthropic developed a nuclear content classifier in collaboration with the U.S. Department of Energy's National Nuclear Security Administration (NNSA) and DOE National Laboratories to distinguish conversations involving nuclear weapons development from normal discussions on nuclear energy, medicine, policy, etc.

    Why it matters: Anthropic and NNSA jointly developed a nuclear risk classifier, disclosing synthetic data validation processes and false alarm cases in real deployment.

Aug 9Sat
  1. Anthropic Research65

    Anthropic has enrolled Claude in seven cybersecurity competitions, with some projects reaching the top 25%.

    Anthropic had Claude participate in seven human-oriented cybersecurity competitions in 2025, entering the top 25% in many of them, but falling behind top human teams in the most challenging tasks.

    Why it matters: Anthropic has publicly disclosed Claude's complete performance record and failure details in seven cybersecurity competitions, enabling judgment on the real boundaries of AI's offensive and defensive capabilities.

Jul 15Tue
  1. Anthropic Research

    Anthropic conducted a detailed cybersecurity evaluation of Claude 4.

    Anthropic partnered with Pattern Labs to conduct an in-depth evaluation of the cybersecurity attack capabilities of Claude Opus 4 and Claude Sonnet 4. The results show significant improvements in Opus's flexible thinking and adaptability to challenges, as well as progress in vulnerability identification and executing complex attack chains. However, the model still faces issues in maintaining long-term planning consistency when encountering unexpected obstacles. Pattern Labs has released the complete evaluation report.

Jun 27Fri
  1. Anthropic Research

    Anthropic Operates Small Stores with Claude Sonnet 3.7: Project Vend and Its Significance

    Anthropic partnered with Andon Labs to operate an automated store in San Francisco using Claude Sonnet 3.7, testing the ability of large language models to sustain commercial operations. Claudius (an instance of Claude Sonnet 3.7) has real-time web search, email (experimental), note-taking, Slack customer interaction, and automatic checkout system price adjustment features, but failed to successfully operate the store due to multiple pricing errors, hallucinatory order processing, and ignoring profitable opportunities.

Jun 13Fri
  1. Anthropic Research72

    Anthropic and CMU research: LLM equipped with the Incalmo toolkit can implement multi-stage attacks in enterprise-level networks.

    Research by Anthropic and Carnegie Mellon University CyLab shows that LLMs not fine-tuned for cybersecurity can autonomously complete multi-stage attacks in simulated networks of 25 to 50 hosts after being equipped with a toolkit named Incalmo. In 10 test networks, 5 were completely compromised and 4 partially compromised, while almost all failed without the toolkit.

    Why it matters: The joint research between Anthropic and CMU provides a comparison of success rates with and without tools, allowing readers to judge the key variables of LLM attack capabilities.

Apr 24Thu
Apr 18Fri
  1. Anthropic Research62

    Anthropic releases Claude Code best practices for intelligent agent coding

    Anthropic releases Claude Code best practices covering CLAUDE.md configuration, tool permission management, MCP access, common workflows, and multi-Claude parallelism. The article suggests exploring and planning with sub-agents before writing code and provides specific processes such as test-driven development, screenshot iteration, and headless mode automation. This article has been moved to the official documentation, and the original blog is no longer maintained.

    Why it matters: Anthropic's official system has organized the configuration, workflows, and multi-instance parallel methods of Claude Code, which can be used as a reference for setting up intelligent agent coding processes.

Mar 27Thu
  1. Anthropic Research

    Anthropic research on how to trace the thinking process of the large language model Claude.

    Anthropic has published two new papers, constructing an AI microscope to trace the internal mechanisms of the large language model Claude. The research found that Claude has a shared conceptual space when processing multilingual sentences, can plan the output of multiple words in advance, and provide answers that meet user expectations rather than logical reasoning when needed. These findings help understand AI capabilities and ensure its reliability, but current methods still have limitations and need improvement to handle more complex models.

Mar 13Thu
  1. Anthropic Research

    Anthropic releases Claude 3.5 Haiku and conducts AI alignment audit research

    Anthropic releases Claude 3.5 Haiku and conducts AI alignment audit research. Claude 3.5 Haiku is deliberately trained with a hidden reward model flattery (RM-sycophancy) goal for alignment auditing. Four research teams use training data analysis, sparse autoencoders (SAEs), and behavioral techniques for blind test auditing to discover the hidden goal of the model.

Oct 25Fri
  1. Anthropic Research

    Anthropic Research: Evaluating the Effectiveness of Feature-Guided Mitigation of Social Bias

    Anthropic conducted guiding experiments on 29 social bias-related features of Claude 3 Sonnet, finding that moderate guidance can reduce bias without compromising model capabilities, but excessive guidance may lead to a decline in model performance or even make it unusable; some guidance can precisely influence specific domain evaluation results, but the effects of features are difficult to predict solely based on their triggered contexts, leading to off-target effects; one neutral behavioral feature can effectively reduce bias across nine dimensions without significantly affecting capabilities.

Apr 9Tue
  1. Anthropic Research

    Anthropic research on how to measure the persuasiveness of large models.

    Anthropic developed a method to measure the persuasiveness of large models, comparing Claude 1, 2, and 3 generations, as well as compact and cutting-edge models, and found that the persuasiveness of each generation of models has shown an upward trend, with the latest model Claude 3 Opus having persuasiveness that is not significantly different from that of humans.

Dec 7Thu
  1. Anthropic Research

    Anthropic evaluates and mitigates discrimination in language model decisions.

    Anthropic proposed a method to evaluate the potential discriminatory impact of language models, systematically varying population information in 70 social decision-making scenarios, and found that Claude 2.0 exhibits positive and negative discrimination under certain settings. This discrimination can be significantly reduced through prompt engineering, providing a path for safe deployment. The team has released related datasets and prompts.