Kaspersky Releases KUMA 4.6 with Knowledge Base, External LLM Support and Automated Regex Generation
Kaspersky has released Kaspersky Unified Monitoring and Analysis Platform (KUMA) version 4.6, bringing significant updates to its SIEM solution used for security event monitoring and analysis.
The most substantial change involves a completely redesigned mechanism for delivering vendor content such as normalizers, correlation rules and other detection materials. The new knowledge base replaces the previous delivery system as the primary tool while retaining the old method for compatibility reasons. Analysts can now search and select content more efficiently for specific use cases, and emergency packages containing detection rules for newly discovered attacks can be distributed faster.
KUMA 4.6 also introduces support for connecting external large language models that are compatible with the OpenAI API. Supported models include GPT-4, Llama 3 and GLM-5.2, which can be hosted either in the cloud or within the customer’s own infrastructure. When deployed on-premises, the Kaspersky Investigation & Response Assistant can perform event analysis and assist with investigations without transmitting data to external services.
One of the standout AI features allows the assistant to automatically generate regular expressions for parsing logs. Security administrators simply provide sample log entries, and the system attempts to create the appropriate Regex patterns itself.
The platform’s integration capabilities have been expanded with support for SFTP and SMB protocols for retrieving data from file repositories, as well as ODBC drivers that enable connections to various database management systems.
Finally, the interface has been refreshed and now includes a dark theme, addressing long-standing requests from corporate users who frequently work during nighttime incident response shifts.
Related articles
Claude Encrypted Thinking Blocks Use Protobuf with Exposed Metadata and AES-GCM Ciphertext
A detailed reverse-engineering of Claude signatures shows that the encrypted reasoning blocks are not opaque containers but structured protobuf messages. The outer envelope contains a 312-byte inner message that holds a 135-byte header, fixed-length nonce and MAC fields, and the actual ciphertext. The header itself reveals the model name such as claude-opus-5, the block type as thinking, and the organizationUuid from the user's Anthropic account. Only the reasoning text is encrypted with AES-GCM, adding exactly 16 bytes for the authentication tag. The analysis covers four protocol versions and notes that organization binding was added in version 15, potentially allowing servers to reject cross-model or cross-organization reuse. The findings provide concrete implications for both the Opus-to-Haiku extraction attack and the leakage of account identifiers in public logs.
Researchers Extract Proprietary Reasoning Traces from Anthropic, OpenAI and Google LLMs, Revealing Hidden Secrets
A team of eight researchers from institutions including ELLIS Institute Tübingen, the Max Planck Institute for Intelligent Systems, Tübingen AI Center, MATS and Snyk published a preprint detailing a practical attack that recovers full reasoning traces from closed LLM APIs. The method requires only two API calls and works by feeding encrypted reasoning blocks from strong models such as Claude Opus 4.8 into weaker models from the same provider, such as Haiku 4.5, which then reproduce the hidden chain-of-thought verbatim. Analysis of 6,708 publicly shared agent logs from GitHub and Hugging Face yielded 315,320 recovered traces containing 704 unique secrets, including 62 API keys, 33 passwords and 24 access tokens that never appeared in visible session output. The attack also enables extraction of internal safety policies, system prompts and detailed harmful planning that providers normally filter from final answers. In addition, the same mechanism can be used in reverse to inject malicious instructions into shared logs that later get replayed by unsuspecting users. The authors recommend treating encrypted reasoning blocks as sensitive secrets and propose cryptographic binding of traces to sessions, users and models.
GhostSplice Technique Lets Malicious MCP Servers Trick AI Coding Agents into Exfiltrating Secrets
GhostSplice is a new technique that allows a malicious MCP server to induce an AI coding agent to leak SSH keys, environment secrets, and source code. The attack splits malicious instructions across tool metadata and responses so the agent reconstructs and executes the full exfiltration plan without detecting an overtly malicious command. Tests showed the method raised compliance rates from an average of 42 percent to 82 percent across eleven models, with some systems moving from zero to 100 percent success. The technique requires the developer to connect the attacker-controlled MCP server and for the agent to already possess read access to the targeted files. Defenses focus on strict allow-listing of MCP servers, least-privilege tool permissions, separation of tool output from instructions, and human approval for sensitive operations. The disclosure aligns with prior warnings about poisoned MCP tool descriptions and agentjacking attacks.
Anthropic Claude Code Auto Mode Launches August 14 with Local Classifier and Permission Rules
Starting August 14, Claude Code will run in auto mode on new sessions for Pro, Max, and Team plans, replacing the allow/deny dialog with a local classifier that evaluates every tool call. The classifier rules are stored locally and contain 103 categories across allow, soft_deny, hard_deny, and environment sections, with the single hard_deny rule focused on data exfiltration spanning over 5,000 characters. Enterprise, API, Bedrock, Vertex, and Foundry deployments remain on opt-in for another month. Auto mode pauses after three consecutive blocks or twenty blocks in a session, and broad allow rules such as python:* are disabled while narrow permissions continue to function. Administrators should populate the twenty environment fields, currently only one-third configured on clean machines, before the rollout date.