AI Coding Tools Under Fire: Grok Build Uploads Entire Git Histories, Claude Code Suspected of Silent Transfers
Security researcher cereblab exposed how Grok Build and Claude Code engage in unauthorized data collection that goes far beyond user expectations, turning routine code assistance into large-scale repository exfiltration.
In a controlled test with a 12 GB local Git repository, Grok Build 0.2.93 created two independent HTTPS channels. The primary channel handled task context at roughly 192 KB, while a background storage channel silently repacked and uploaded the entire .git directory in 75 MB chunks, consuming 5.10 GiB of traffic to a Google Cloud Storage bucket named grok-code-session-traces. This produced a 27800-fold difference between expected and actual outbound volume.
The upload logic activated regardless of explicit user instructions such as "do not read" placed on marker files, proving that the behavior is a hardcoded data-collection routine rather than an artifact of the model's understanding. Privacy controls proved ineffective: the client-side improve_model_enabled flag is completely decoupled from the server-side trace_upload_enabled flag, allowing uploads to continue even after users disable model-improvement features.
Although xAI later disabled the uploads via remote configuration on 13 July, the full repository-upload code remained present in the 0.2.99 binary, merely gated by a server flag. The exposed Git history includes deleted .env files, credentials, connection strings, and commit records that reveal vulnerability-fix timelines, enabling attackers to infer unpatched issues in similar codebases.
Parallel findings emerged for Claude Code, whose client maintains multiple WebSocket connections that periodically transmit file paths, dependency trees, and code fragments even without an active coding task. No local logs, switches, or data-flow documentation are provided, and the closed-source binary prevents independent verification of additional hidden routines.
A side-by-side traffic audit of major tools produced clear results:
- Grok Build: full .git directory, Git history packaged, privacy switch ineffective, confirmed silent upload.
- Claude Code: suspected file-level and metadata exfiltration, details undisclosed, silent-transfer behavior confirmed.
- Codex: only current context fragments, no Git packaging, privacy controls effective, no anomalous reports.
- Gemini: no active uploads detected.
Analysts attribute the pattern to fundamental conflicts between commercial model-training demands and user data sovereignty, excessive server-side control over local clients, and the absence of mandatory third-party audits for closed-source AI binaries.
Recommended defenses include physical network blocking of known endpoints, running tools inside Docker containers that mount only read-only directories without .git, pre-processing repositories with git filter-repo, deploying eBPF monitoring with alerts for sessions exceeding 10 MB, and favoring auditable open-source options such as Continue.dev or locally hosted Ollama models.
Related articles
PentesterFlow Launches Open-Source AI CLI Tool for Penetration Testers and Bug Bounty Hunters
PentesterFlow is a new open-source, human-in-the-loop AI command-line tool designed specifically for penetration testers and bug bounty hunters. It automates the full workflow from reconnaissance to report generation while requiring explicit analyst approval before executing sensitive commands. The tool addresses common issues in agentic AI security tools such as hallucinations, weak context retention, and poor tool integration by incorporating built-in pentesting skills and evidence-based vulnerability confirmation. It supports connections to local or hosted LLMs including Ollama, Gemini, Groq, and others, and features continuous local learning that stores user preferences and lessons without retraining models. A key differentiator is its integration with Burp Suite and a permission-based execution model that includes a YOLO mode for isolated environments. The project positions itself as a transparent alternative to fully autonomous tools like PentAGI and PentestGPT.
Optimizing Cybersecurity Content for LLMs: How Sites Can Enter Generative AI Answers
Search engines and AI services like ChatGPT, Gemini, Perplexity, Copilot and Google AI Overviews increasingly deliver synthesized answers instead of link lists. For cybersecurity publishers this changes competition because high traditional rankings no longer guarantee visibility or accurate citation. The article explains GEO, AEO and LLMO practices, shows how material moves through indexing, fragment selection and summarization stages, and stresses the need for self-contained facts that survive extraction and paraphrasing. It provides concrete writing frameworks for vulnerability reports, including required fields such as CVE identifiers, affected versions, attack conditions and real-world exploitation evidence. Technical requirements cover correct robots.txt handling for Googlebot, OAI-SearchBot, GPTBot and Bingbot plus the use of IndexNow for rapid updates. The piece also warns about poisoning risks, prompt injection and slopsquatting attacks that can feed false data into generative systems.
LangGraph Architecture Combines Hybrid RAG with YARA and Sigma Engines for Streaming Log Analysis
A new architectural pattern integrates LangGraph with a hybrid RAG system and deterministic signature engines to process large volumes of unstructured cybersecurity logs efficiently. The pipeline uses Vector for chunking logs into 250-line segments with 20-line overlap, Kafka for streaming, and an 8-node asynchronous DAG that runs AI and rule-based branches in parallel. Agent 1 groups events and generates hypotheses, while a hybrid RAG module performs query reformulation, vector plus BM25 search with 0.6/0.4 weighting, and LLM re-ranking against a translated MITRE ATT&CK knowledge base stored in ChromaDB. Parallel YARA and Sigma engines scan parsed logs using custom text-based rule implementations, with automatic YARA rule generation triggered when coverage gaps are detected. Final aggregation occurs in Agent 3, which validates findings, deduplicates confirmed incidents, and routes unconfirmed events for manual review while storing reports in PostgreSQL. Tested on a 43-minute synthetic dataset containing 38 MITRE techniques, the system achieved 85.7% precision and 78.9% recall at 3.5 lines per second using Gemini 2.5 Flash.
Local LLM Deployment for SOC: How Many Incidents Can One NVIDIA RTX PRO 6000 Handle?
In the second part of the experiment, R-Vision analysts tested the Qwen3.5-122B-A10B-GPTQ model running locally with vLLM on an NVIDIA RTX PRO 6000 Blackwell Max-Q GPU with 96 GB VRAM. The evaluation moved from synthetic stress tests to realistic SOC workloads using anonymized real incidents from their internal operations center. Tasks included incident ranking, summarization, similarity search, retrospective analysis, and preliminary verdict generation within the R-Vision SOAR orchestration pipeline. Two load scenarios were modeled: a calm shift with 10-15 incidents per hour handled by 3-5 L1/L2 analysts, and a peak scenario with 50-100 incidents arriving in a short period involving 5-7 analysts. Resources were split between a high-priority interactive chat pool and a lower-priority background SOAR pool using an AI Gateway layer to manage queues, context length, and KV-cache usage. Results showed that one GPU can process up to 5 incidents simultaneously in the background pipeline, delivering a theoretical maximum throughput of 300 incidents per hour under the tested conditions and prompt profiles.