First reported · updated · 2 reports github.com
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First reported github.com
customhouse/docs/false-positives.md at main · vineetpant/customhouse
customhouse is an MCP proxy that blocks prompt-injection exfiltration by tracking data provenance rather than inspecting content: once a session receives a result from an untrusted upstream MCP server, calls that move money or send data out are refused for the rest of the session. The write-up reports it blocked 11 of 11 injection scenarios but also flagged 4 of 10 benign workflows that used a sink, and analyzes each false positive. Details →First reported github.com
Strip hidden unicode tags from python strings in order to avoid malicious prompt injection attacks
A GitHub gist titled "strip-unicode-tags.py" by user r-leyshon provides a Python script to strip hidden Unicode tag characters from strings in order to defend against malicious prompt injection attacks that abuse invisible/hidden Unicode characters. Details →First reported darkreading.com
New Tool Traces AI Videos Back to Their Source
UC Riverside researchers built SAGA (Source Attribution of Generative AI videos), a framework that not only detects whether a video is AI-generated but also identifies the specific generative model, its version, and the development team for forensic attribution. The tool aims to counter deepfake-driven disinformation, impersonation, and social-engineering threats such as fraudulent deepfake job applicants. Details →First reported towardsdev.com
I Built a Prompt Injection Detector Using Go’s Standard Library
Cheikh seck describes building a prompt injection detector in Go using only the standard library — 29 regex rules and no ML models — tested against a product page seeded with 12 attack techniques drawn from a 2026 CrowdStrike prompt-injection taxonomy report. The accompanying code is published on GitHub (github.com/cheikh2shift/go-snippets/tree/main/prompt-injection). Details →First reported github.com
GitHub - GiovanniGatti/cve-bench: A benchmark for evaluating AI agents on fixing real-world security vulnerabilities.
cve-bench is a benchmark by GiovanniGatti for evaluating AI agents on their ability to fix real-world security vulnerabilities, shipping a Docker-based harness, results, and a write-up comparing model performance across CVEs such as CVE-2026-33175, CVE-2026-42561, CVE-2026-40864, and CVE-2026-30930. Details →First reported github.com
GitHub - cgrtml/reasongate: Explainable security gate for LLM apps — blocks prompt injection with an auditable reason for every decision.
ReasonGate is an open-source explainable security gate for LLM applications that aims to block prompt injection while emitting an auditable reason for every allow/deny decision. Beyond text detection, it adds a capability-based 'agent action gate' (framed around the lethal-trifecta) that blocks tool calls by argument taint even when detection misses reworded attacks, with a live playground and a documented threat model describing its guarantees and non-guarantees. Details →First reported github.com
GitHub - davidmatousek/tachi: Threat modeling and AI-reasoning vulnerability detection harness for Claude Code — STRIDE + AI + MAESTRO
Tachi is an open-source GitHub project by davidmatousek providing a threat-modeling and AI-reasoning vulnerability detection harness for Claude Code, combining STRIDE, AI-specific, and MAESTRO threat frameworks. The repo references OWASP Top 10 for LLM and Agentic Applications and is actively developed with numerous releases. Details →First reported github.com
GitHub - beebeeVB/trajeckt: A causal firewall for AI agents: blocks multi-step tool-call chains that leak data, even when every call is individually allowed.
trajeckt is an open-source GitHub project described as a causal firewall for AI agents that blocks multi-step tool-call chains capable of leaking data even when each individual call is permitted. The repository includes SDKs, source, tests, benchmarks, and deployment configs. Details →First reported github.com
GitHub - vinerya/driftGuard: Embedding-based response drift detection for LangChain agents.
driftGuard is a Python package providing embedding-based response drift detection for LangChain agents, intended to monitor and detect when agent responses deviate over time. Details →First reported github.com
GitHub - bastion-soft/pi-detector-bench: Open, model-agnostic benchmark for prompt-injection detectors — scored on both axes (attack catch-rate and false positives on real traffic), threshold-agnostic, and reproducible from raw scores.
pi-detector-bench is an open, model-agnostic benchmark from bastion-soft for evaluating prompt-injection detectors, scoring both attack catch-rate and false positives on real traffic, with threshold-agnostic, reproducible results. It is published as a runnable GitHub repo with notebooks, scripts, and results. Details →First reported github.com
GitHub - Lelu-ai/lelu: Open source authorization engine for AI agents. Confidence-aware gating · Human-in-the-loop review · Policy-as-code · Full audit trail
Lelu is an open-source authorization engine for AI agents offering confidence-aware gating, human-in-the-loop review, policy-as-code, and a full audit trail. It adds decision types (allow/deny/human_review/compute), payload hashing, and policy digests to control and audit agent tool actions. Details →First reported github.com
GitHub - can995835646-byte/SentryGuard: Python CLI to detect Agentjacking injection attacks in Sentry error events
SentryGuard is a Python CLI that scans Sentry error events to detect 'Agentjacking' injection attacks — indirect prompt-injection payloads (e.g. system_prompt_injection, base64_shell_eval, 'ignore previous instructions', 'ADMIN OVERRIDE') that could trigger AI agents consuming error data. It also offers a sanitize command to strip system-prompt-style and prompt-override phrases from events. The repo is gated behind a Gumroad license check. Details →First reported jackdavis.net
Maintaining Privacy With Agents: What Actually Works When Sensitive Data Is Part of the Workflow
A write-up releasing `agent-privacy`, an open-source privacy-control layer for CLI-style agent harnesses that intercept sensitive data (PII, identifiers) before it becomes model-visible context. It uses prompt/pre-tool/post-tool hooks with four actions — allow, redact, handoff, block — and integrates a local filter service, OpenAI Privacy Filter, and local Qwen models, with an operational pii-guard report covering ~6,343 screening decisions. Details →How the wire is made
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