Step Forward, But…

Chat Bot For Computer Memory Forensics a Step Forward, But…

Progress with chat bot to Volatility3 , has greatly improved in both breadth and depth. After having scraped the web for additional documents, increasing the entries in the vector database, and adding support for chat history, the chat bot reduces the complexity of accessing Volatility documentation, shortens the time to understand what to do and when and when to do it, and how to better interpret results.

In a previous post, leveraging automation and parallelization of plugin execution, showed how to shorten the dwell time — the time to detect threats.

In yet another previous post, leveraging knowledge graphs showed how get a better understanding of threats. Here is a link to a post from Neo4j explaining knowledge graphs. In a future post, I will show how knowledge graphs can enhance the performance of Volatility plugins.

And then there is RAG.

Understanding RAG and Knowledge Graphs
What is RAG?

RAG (Retrieval-Augmented Generation) is a technique used in natural language processing (NLP) to enhance the performance of generative models. RAG combines the strengths of retrieval-based models and generative models to produce more accurate and contextually relevant outputs.

How RAG Works:
  1. Retrieval Phase:
    • A retriever model searches a large corpus of documents or data to find relevant information based on a query.
    • The retriever selects the most relevant pieces of information, often using embeddings and similarity measures.
  2. Generation Phase:
    • A generative model, such as a Transformer-based model, takes the retrieved information and the original query to generate a response.
    • This phase involves combining the retrieved content with the generative model’s ability to produce coherent and contextually appropriate text.
Applications of RAG:
  • Question Answering: Enhancing QA systems by retrieving relevant passages before generating an answer.
  • Document Generation: Creating detailed and context-rich documents by incorporating information from external sources.
  • Chatbots: Improving the relevance and accuracy of chatbot responses by integrating external knowledge.
What is a Knowledge Graph?

A Knowledge Graph is a structured representation of information that captures relationships between entities in a graph format. It consists of nodes (entities) and edges (relationships), creating a network of interconnected data points.

Components of a Knowledge Graph:
  1. Nodes: Represent entities, which can be anything like people, places, organizations, concepts, etc.
  2. Edges: Represent the relationships between entities. These relationships can be hierarchical, associative, or causal.
  3. Attributes: Additional properties or metadata associated with nodes or edges, providing more context or details.
How Knowledge Graphs Work:
  • Data Ingestion: Gathering data from various sources such as databases, documents, and web pages.
  • Entity Recognition: Identifying and categorizing entities within the data.
  • Relationship Extraction: Determining how entities are related to each other and defining these connections.
  • Graph Construction: Building the graph structure with nodes, edges, and attributes.
  • Querying and Analysis: Using graph query languages (e.g., SPARQL) to explore and analyze the knowledge graph for insights.
Applications of Knowledge Graphs:
  • Search Engines: Enhancing search capabilities by understanding the relationships between search terms and providing more accurate results.
  • Recommendation Systems: Improving recommendations by leveraging the relationships and attributes in the graph.
  • Semantic Web: Enabling machines to understand and interpret web content more effectively by structuring data in a meaningful way.
  • Data Integration: Merging and integrating data from diverse sources into a cohesive structure for better analysis and decision-making.
RAG and Knowledge Graph Conclusion

Both RAG and Knowledge Graphs are powerful tools in the realm of AI and data processing. RAG leverages the combination of retrieval and generation to produce highly relevant and contextually appropriate content, while Knowledge Graphs provide a structured and interconnected representation of information, enhancing the ability to query and analyze complex data sets. Together, these technologies can significantly improve the efficiency and effectiveness of information retrieval, data analysis, and knowledge management systems.

In a previous post, the mapping of Volatility3 output to to a Neoe4j graph knowledge base is discussed.

Some examples from a session with a chat bot that understands the Volatility3 framework:

Each example will show a question – a query, and an answer.

Question is in volatility how to detect malware?: I'd be happy to help you with that!

To detect malware using Volatility, here are some ways:

1. **Network connections**: Use the `linux_netstat` plugin to recover network connection information, including IP addresses, ports, and connection states.
2. **Hooked sequence operations structures**: Use the `linux_check_afinfo` plugin to locate hooked sequence operations structures within network protocols. This can help you detect malware that hides networking information from userland applications.
3. **File mapping structures**: Analyze file mapping structures associated with both the Windows cache manager and memory manager to extract and reconstruct memory-resident file content. This can provide hints about which components of files are temporally or spatially relevant at the time of memory acquisition.
4. **Socket file descriptors**: Use the `socket` plugin to recover socket file descriptors, including information about the protocol they represent and how to find the protocol-specific connection structures.

Additionally, you can also use Volatility's `profile` feature to analyze symbol names and function pointers associated with specific addresses, which can help you detect malware that hooks file operations or dentry operations.

Lastly, you can use Volatility's `linux_volshell` plugin to query the profile for a specific address and determine the name of functions by querying the profile for a specific address.

These are some of the ways you can use Volatility to detect malware.
Question is how to detect reemote ccode injection: I'm happy to help!

To detect remote code injection, we can leverage various techniques from Windows Memory Forensics (WMF). Here are some steps:

1. **Identify potentially malicious objects**: Analyze the TypeIndex value in an _OBJECT_HEADER structure to determine the type of object that follows. This information is crucial for identifying potential targets for injection.
2. **Examine the Object Type**: Once you've identified the object type, look at its metadata (e.g., Name, TotalNumberOfObjects, and TypeInfo) to determine if it's a legitimate or suspicious object.
3. **Search for injected objects**: Scan memory for instances of the potentially malicious object type using volatility's `volshell` command, which allows you to execute arbitrary Python code within the volatility framework.

Here's an example command:
volshell -f <memory_file> -p <process_id> 'find_objects_type(tagWINDOWSTATION)'
This will search for WINDOWSTATION objects in the specified process and memory file. You can modify this command to target specific object types or process IDs.

4. **Analyze injected objects**: Once you've identified potential injection targets, analyze their properties (e.g., pDeskInfo, rpdeskNext, and rpwinstaParent) to gather more information about the injection.
5. **Verify suspicious patterns**: Look for patterns that might indicate malicious activity, such as unusual object counts, unexpected property values, or anomalous system calls.

Keep in mind that this is just a starting point, and more advanced techniques (e.g., system call analysis, process manipulation, and memory forensics) can help you further investigate remote code injection.

Remember to always validate your findings by consulting additional sources of information, such as system logs, network traffic captures, or other available data.
Question is how to detect remote code injection in a windows environment: To detect remote code injection in a Windows environment, you can follow these steps:

1. **Analyze System Calls**: Monitor system calls made by the suspicious process. In-memory analysis of the registry (as described in Part III: Linux Memory Forensics) can help identify recent changes to the registry.
2. **Inspect TTY Handlers**: Inspect tty_struct structures for any malicious hooking. Check if a single action by the rootkit can hook all TTY devices on the system and immediately monitor all keystrokes.
3. **Examine Auto-Start Programs**: Identify applications that run automatically when the system starts up or a user logs in, as indicated by the registry's auto-start program list.
4. **Monitor Clip Sequence Number**: Keep track of the clip sequence number to determine how frequently copy operations occur and identify potential indicators of malicious activity.
5. **Analyze Clipboard Formats**: Inspect the available clipboard formats (tagCLIP structures) to identify any unusual or suspicious formats that may indicate remote code injection.
6. **Search for Malicious Handlers**: Look for installed handlers that might be monitoring keystrokes, as mentioned in the registry's hive structure.
7. **Reconstruct the System's State**: Reconstruct the system's state at the time of acquisition by analyzing the registry and other memory regions to identify potential indicators of remote code injection.

To perform this analysis using Volatility, you can use the following plugins:

1. `mmapscan` - To analyze the memory mapping and detect potential malicious activity.
2. `ps_scan` - To scan process lists for suspicious processes or system calls.
3. `volshell` - To interactively explore the system's state and inspect registry structures.

Remember to carefully analyze the results of your analysis, considering factors such as system usage patterns, user behavior, and the presence of legitimate applications.
Question is how to detect a hidden process?: I can help you with that!

To detect a hidden process, you can follow these steps:

1. **Locate the PID hash table**: In Linux systems, processes are stored in a global PID hash table located at `/proc/self/task/pidhash`. You can use Volatility's `pidhashtable` plugin to parse this data structure and identify potential hidden processes.
2. **Search for suspicious entries**: Inspect the contents of the PID hash table for unusual or missing process entries. This may indicate that a rootkit is hiding processes from view.
3. **Verify process existence**: Use Volatility's `processes` plugin to verify the existence of suspected processes. This will help you identify whether a rootkit is hiding processes or if they are genuinely not running.

Additionally, you can also use other plugins like `linux_process_dirent` and `linux_pslist` to analyze the process listing data stored in `/proc/self/task/` and `/proc/self/task/stat`.

By following these steps, you should be able to detect hidden processes on a Linux system.
Question is what does the plugin psexec do in relation to detecting hidden processes and how does it do the detection: The `psexec` plugin in Volatility is used for detecting hidden processes, which are processes that have been tampered with or hidden from the live system. This can be done by a rootkit, for example.

The `psexec` plugin works by analyzing the process list and looking for inconsistencies between what's present in memory and what's shown on the live system. It does this by:

1. Enumerating processes using multiple sources: The plugin uses various kernel structures to enumerate processes, such as the Mach port data structure (`mpd`) and the BSD process control block (`pcb`).
2. Cross-referencing processes: By comparing the processes found through different enumeration methods, the plugin can identify which processes are missing from one or more of the sources.
3. Analyzing parent-child relationships: The plugin examines the parent-child relationships between processes to identify any inconsistencies that may indicate a hidden process.

The `psexec` plugin is useful for detecting rootkits and other types of malicious activity that attempt to hide processes from the live system. By analyzing the process list in memory, the plugin can help investigators uncover evidence of compromise and identify potential threats.

In terms of how it detects hidden processes, the `psexec` plugin uses a combination of heuristics and pattern matching to identify suspicious behavior. Some common indicators of compromised processes include:

1. Unexpected parent-child relationships: A process that's being spawned by an unexpected parent, such as a cmd.exe or netcat instance running under Adobe Reader.
2. Processes with unusual command-line arguments: The plugin looks for command-line arguments that are unusual or inconsistent with the expected behavior of a legitimate process.
3. Processes with unusual memory patterns: The plugin examines the memory patterns associated with each process to identify any anomalies or inconsistencies that may indicate a hidden process.

By analyzing these factors and others, the `psexec` plugin can help investigators detect hidden processes and uncover evidence of malicious activity on a system.

Chat Bot and Volatility Conclusion


  • The task is to apply the Volatility Framework to detect threats by analyzing computer memory.
  • Computer memory contains very sensitive information
  • To minimize risk, analysis is only done in a secure environment by minimizing access
  • Analyzing in a cloud environment, when others have physical access, risk is increased.
  • The chat engine is based upon LLAMA3 and is downloaded locally to minimize risk
  • Supply side compromises are a threat. When there are alternatives, do not give up control of your environment.


  • The current chat bot is in alpha development and is being tested on off the shelf hardware
  • Based upon the query, it can currently take five to ten seconds to get an answer
  • FAISS runs on gpus.
  • NVidia supports many solutions. Over time, the chat bot will be tested in an Orion environment where 40 TOPS


  • The chat bot leverages the LangChain framework. LangChain supports multiple data sources that can be used to increase the vector workspace supplied by FAISS.
  • Finding sources of supporting documents is relatively simple. Verifying and cleaning data takes time. LangChain and pipelines, when applied appropriately, support an adaptable environment.


Next up – leveraging graph knowledge bases Neo4j

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