Adversarial Prompt Injection and Misleading Explanations

Validate core concepts, establish threat models, and design the architecture for GLO, MCDE, and AEFS.

Steps

• Threat Landscape Analysis(4 wks)
  Survey existing CoT jailbreaks, collect benchmark datasets (e.g., D‑REX, XSTest), and formalize attack scenarios.
• Prototype GLO Interface(4 wks)
  Design a lightweight, low‑latency sensor API that hooks into model inference pipelines to capture attention, embeddings, and logits.
• Mechanistic CoT Decomposition Prototype(4 wks)
  Implement a rule‑based parser to split CoT into atomic steps and map them to a preliminary reliability graph.
• Fidelity Metric Design(4 wks)
  Define the AEFS scoring function, including divergence thresholds and penalty weights.

Milestones

Risks

• Incomplete coverage of emerging jailbreak techniques
• High latency of GLO sensor in real‑time inference

Build end‑to‑end prototypes of GLO, MCDE, and AEFS; validate detection accuracy on benchmark attacks.

Steps

• Integrate GLO with LLM Inference Engine(6 wks)
  Hook the sensor into a production‑grade inference stack (e.g., Triton Inference Server).
• Expand Reliability Graph(6 wks)
  Populate the graph with known safe patterns and adversarial signatures using unsupervised clustering.
• AEFS Engine Implementation(6 wks)
  Implement dynamic scoring, threshold tuning, and alert generation.
• Evaluation Pipeline(6 wks)
  Automate testing against D‑REX, XSTest, and custom adversarial prompts; compute precision/recall of deception detection.

Milestones

Risks

• Reliability graph may overfit to known attacks and miss novel patterns
• Prototype may not scale to larger models (e.g., 70B+)

Dependencies

• Phase 1 Deliverables

Extend the defense to multi‑agent systems, implementing cryptographic signing of explanation fragments and ledger‑based cross‑validation.

Steps

• Design Ledger Architecture(3 wks)
  Select a lightweight blockchain (e.g., Tendermint) and define data schema for signed explanation fragments.
• Agent Attestation Module(3 wks)
  Implement hardware attestation and cryptographic key management for each agent.
• Cross‑Validation Engine(4 wks)
  Build logic to compare fragments across agents, flag inconsistencies, and trigger mitigation actions.
• Sybil Resistance Tests(2 wks)
  Simulate Sybil attacks and evaluate detection efficacy.

Milestones

Risks

• Ledger scalability may limit real‑time verification
• Privacy concerns around on‑chain data exposure

Dependencies

• Phase 2 Deliverables

Embed AEFS scores into a reinforcement‑learning controller that tunes the safety reward function in real time.

Steps

• Reward Model Design(3 wks)
  Define intrinsic reward signals based on AEFS divergence and policy entropy.
• RL‑Fine‑Tuning Pipeline(4 wks)
  Set up PPO/QLearning loop that retrains the LLM policy on detected deceptive prompts.
• Adversarial Dataset Augmentation(3 wks)
  Generate new attack samples using generative models and incorporate them into the training loop.
• Safety‑Utility Trade‑off Calibration(2 wks)
  Tune reward weights to balance refusal rates and helpfulness metrics.

Milestones

Risks

• Reward hacking leading to unintended behavior
• Training instability when mixing large‑scale LLMs with RL

Dependencies

• Phase 3 Deliverables

Deploy the full defense stack in a real‑world multi‑agent application, monitor performance, and prepare for scaling.

Steps

• Pilot Deployment(6 wks)
  Integrate GLO, MCDE, AEFS, MAVP, and CAFL into an existing multi‑agent platform (e.g., autonomous logistics coordination).
• Operational Monitoring(4 wks)
  Set up dashboards for latency, detection rates, and safety‑reward metrics; implement alerting.
• Compliance & Privacy Review(4 wks)
  Audit data handling, ledger privacy, and compliance with GDPR/CCPA.
• Scale‑Up Plan(4 wks)
  Design sharding strategy for GLO sensors and ledger nodes to support > 1M requests/day.

Milestones

Risks

• Unexpected production bottlenecks
• Regulatory changes affecting data retention

Dependencies

• Phase 4 Deliverables