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Project 2: Adversarial Robustness & Security

Conducting a rigorous security assessment of the CNS 2.0 system to test its resilience against sophisticated adversarial attacks and develop novel defenses.

The Challenge: From Benign Errors to Malicious Attacks

Standard evaluation tests a system’s performance under normal, benign conditions. However, a system designed to operate on real-world information from the open internet will inevitably face adversaries who wish to manipulate its conclusions. These are not random errors; they are carefully crafted attacks designed to exploit a system’s reasoning and data-processing vulnerabilities to produce a desired, incorrect, and potentially harmful output.

As detailed in our Ideas Paper (Sec 8.4), these attacks can include:

  • Subtle Evidence Manipulation: Slightly altering data points, misquoting sources, or fabricating “plausible” data to support a false claim.
  • Coordinated Disinformation: Ingesting a large number of seemingly independent narratives that all subtly point towards the same false conclusion, overwhelming simple quality filters.
  • Logic Bomb Attacks: Crafting a set of inputs that appear sound on the surface but contain a hidden logical contradiction, fallacy, or structural weakness designed to confuse the synthesis engine or cause a system failure.

The Vision: A Resilient, Hardened, and Trustworthy System

This research project aims to move beyond standard evaluation to conduct a rigorous adversarial robustness and security assessment of CNS 2.0. The goal is to proactively identify and remediate vulnerabilities before they can be exploited by malicious actors. We seek to build a system that is not only accurate under ideal conditions but is also hardened and resilient in the face of determined opposition, making it a truly trustworthy cognitive tool.

Key Research Questions

  1. What are the primary adversarial attack vectors against the CNS 2.0 architecture, from the ingestion pipeline to the final synthesis?
  2. How effective are the system’s built-in defenses (e.g., the GroundingCritic, the LogicCritic) at detecting and rejecting manipulated inputs, especially when attacks are subtle and coordinated?
  3. Can we develop and validate new, specific defense mechanisms that counter sophisticated, coordinated attacks and provide a measurable increase in system security?

Proposed Methodology

This research will be conducted using a structured “red team” approach, where our own experts actively attempt to deceive and break the system to uncover its weaknesses.

Stage 1: Threat Modeling

We will begin with a systematic analysis of the entire CNS 2.0 workflow to identify potential weak points. This involves creating a formal “threat model” that maps potential attack vectors to specific system components. This model will categorize threats by type (e.g., data poisoning, model evasion, logic manipulation), potential impact, and estimated difficulty of execution.

Stage 2: Red Team Attack Simulation

A dedicated “red team” will design and execute a suite of adversarial attacks based on the threat model. This goes beyond simple noise injection to simulate the methods of a sophisticated adversary.

  • Evidence Forgery: Crafting SNOs with fabricated evidence that is semantically plausible and designed to bypass the GroundingCritic. This includes generating fake citations or creating synthetic data tables.
  • Fallacy Injection: Designing reasoning graphs (G) that employ subtle logical fallacies (e.g., circular reasoning, strawman arguments) that may not be immediately obvious to the GNN-based LogicCritic.
  • Narrative Flooding: Simulating a coordinated disinformation campaign by generating and ingesting dozens of low-quality but superficially consistent SNOs. The goal is to see if the system can be pushed towards a false consensus by the sheer volume of reinforcing narratives.

Success will be measured by the system’s ability to either reject the malicious SNOs outright or produce a final synthesis that correctly identifies and flags the manipulation.

Stage 3: Defense Development and Hardening

Based on the red team’s findings, we will develop, implement, and test new defense mechanisms.

  • Consistency Clustering: A novel algorithm that analyzes the entire SNO population to detect clusters of narratives that are “too similar,” which can be an indicator of a coordinated narrative-flooding campaign.
  • Source Reputation and Provenance Scoring: An enhancement to the TrustScore that incorporates a dynamic reputation for evidence sources. Sources that are frequently associated with low-scoring or rejected SNOs will see their reputation diminished, making them less influential in future syntheses.
  • Enhanced Critic Logic: Upgrading the GroundingCritic to perform more robust cross-verification against external knowledge bases and training the LogicCritic on a new dataset of adversarial fallacies.

The hardened system will then be re-evaluated by the red team, creating an iterative cycle of attack, defense, and re-evaluation to continuously improve system security.

Expected Contribution

This research is essential for preparing CNS 2.0 for real-world deployment in high-stakes environments. The expected contribution is twofold:

  1. A detailed security and robustness analysis of a complex AI reasoning system, providing a public record of its strengths and weaknesses.
  2. A generalizable framework and a set of novel defensive techniques (like Consistency Clustering) for making any complex AI reasoning system more robust and trustworthy.

This work is critical for building the public and expert trust necessary for the responsible adoption of automated knowledge synthesis technologies.