Simon WillisonModify

2026-04-05 12:07:13

Release: scan-for-secrets 0.2

• CLI tool now streams results as they are found rather than waiting until the end, which is better for large directories.
• -d/--directory option can now be used multiple times to scan multiple directories.
• New -f/--file option for specifying one or more individual files to scan.
• New scan_directory_iter(), scan_file() and scan_file_iter() Python API functions.
• New -v/--verbose option which shows each directory that is being scanned.

2026-04-05 11:39:33

Release: scan-for-secrets 0.1.1

• Added documentation of the escaping schemes that are also scanned.
• Removed unnecessary repr escaping scheme, which was already covered by json.

2026-04-05 11:27:13

Release: scan-for-secrets 0.1

I like publishing transcripts of local Claude Code sessions using my claude-code-transcripts tool but I'm often paranoid that one of my API keys or similar secrets might inadvertently be revealed in the detailed log files.

I built this new Python scanning tool to help reassure me. You can feed it secrets and have it scan for them in a specified directory:

uvx scan-for-secrets $OPENAI_API_KEY -d logs-to-publish/

If you leave off the -d it defaults to the current directory.

It doesn't just scan for the literal secrets - it also scans for common encodings of those secrets e.g. backslash or JSON escaping, as described in the README.

If you have a set of secrets you always want to protect you can list commands to echo them in a ~/.scan-for-secrets.conf.sh file. Mine looks like this:

llm keys get openai
llm keys get anthropic
llm keys get gemini
llm keys get mistral
awk -F= '/aws_secret_access_key/{print $2}' ~/.aws/credentials | xargs

I built this tool using README-driven-development: I carefully constructed the README describing exactly how the tool should work, then dumped it into Claude Code and told it to build the actual tool (using red/green TDD, naturally.)

Tags: projects, security, agentic-engineering, coding-agents, ai-assisted-programming, claude-code

2026-04-05 08:32:11

Release: research-llm-apis 2026-04-04

I'm working on a major change to my LLM Python library and CLI tool. LLM provides an abstraction layer over hundreds of different LLMs from dozens of different vendors thanks to its plugin system, and some of those vendors have grown new features over the past year which LLM's abstraction layer can't handle, such as server-side tool execution.

To help design that new abstraction layer I had Claude Code read through the Python client libraries for Anthropic, OpenAI, Gemini and Mistral and use those to help craft curl commands to access the raw JSON for both streaming and non-streaming modes across a range of different scenarios. Both the scripts and the captured outputs now live in this new repo.

Tags: llm, apis, json, llms

2026-04-04 10:20:17

[GitHub] platform activity is surging. There were 1 billion commits in 2025. Now, it's 275 million per week, on pace for 14 billion this year if growth remains linear (spoiler: it won't.)

GitHub Actions has grown from 500M minutes/week in 2023 to 1B minutes/week in 2025, and now 2.1B minutes so far this week.

— Kyle Daigle, COO, GitHub

Tags: github, github-actions

2026-04-04 07:59:08

Vulnerability Research Is Cooked

Within the next few months, coding agents will drastically alter both the practice and the economics of exploit development. Frontier model improvement won’t be a slow burn, but rather a step function. Substantial amounts of high-impact vulnerability research (maybe even most of it) will happen simply by pointing an agent at a source tree and typing “find me zero days”.

Why are agents so good at this? A combination of baked-in knowledge, pattern matching ability and brute force:

You can't design a better problem for an LLM agent than exploitation research.

Before you feed it a single token of context, a frontier LLM already encodes supernatural amounts of correlation across vast bodies of source code. Is the Linux KVM hypervisor connected to the hrtimer subsystem, workqueue, or perf_event? The model knows.

Also baked into those model weights: the complete library of documented "bug classes" on which all exploit development builds: stale pointers, integer mishandling, type confusion, allocator grooming, and all the known ways of promoting a wild write to a controlled 64-bit read/write in Firefox.

Vulnerabilities are found by pattern-matching bug classes and constraint-solving for reachability and exploitability. Precisely the implicit search problems that LLMs are most gifted at solving. Exploit outcomes are straightforwardly testable success/failure trials. An agent never gets bored and will search forever if you tell it to.

The article was partly inspired by this episode of the Security Cryptography Whatever podcast, where David Adrian, Deirdre Connolly, and Thomas interviewed Anthropic's Nicholas Carlini for 1 hour 16 minutes.

I just started a new tag here for ai-security-research - it's up to 11 posts already.

Tags: security, thomas-ptacek, careers, ai, generative-ai, llms, nicholas-carlini, ai-ethics, ai-security-research