Adaptive Attacks Expose SLM Vulnerabilities and Qualitative Insights

Links table

Part 1: Summary and introduction

Part 2: The background

Part 3: attacks and counter measures

Part 4: Experimental Preparation

Part 5: Data sets and evaluation

Part 6: attack, counter -procedure parameters, basic line: random disorders

Part 7: Results and discussion

Part 8: Transfer attacks and anti -measures

Part 9: Conclusion, restrictions, and clarification of morals

Part 10: Approach: Training and Evaluation on Sound encoding

Part 11: Approach: multiple attacks, training data, and the effect of random noise on help

Part 12: Approach: Adaptive attacks and qualitative examples

A.6 Adaptive attacks

In this section, we report the results using adaptive attacks, as the attacker has the knowledge of any defense mechanism used in the system. We use α = 0.0001 (equivalent 1) We also found that the attacker needs a larger step in the presence of a defense to produce successful attacks. From Table 10, we see that the attacks become less successful in the presence of a defense. Also, the adaptive attacker needs to add more cognitive disorders (average SPR) in the presence of a defense. This clearly indicates that the simple defense before treatment can provide a degree of durability against the rivalry attacks.

Also, from Figure 5, we note that the presence of a defense in the system makes the attacks less effective under limited attack budgets. For a specific attack budget of repetition T = 50, only 60 % of attacks on the system with TDNF defense, compared to ∼80 % for a defense system. However, note that these attacks were carried out with a limited attack budget of T = 100 repetitions. The harmful actor with a larger attack budget is likely to be caused by a higher imprisonment.

A.7 specific examples

Table 11 compares SLM model responses at home to harmful examples. We show the scenarios in which the models produce safe content although it is not related, as well as a safe content with a relevant understanding of the input sound. In general, the internal SLM model explains the ability to better understand speech.

Table 12 compares models on different help questions across different aspects of interest. We note that the SLM model at home is sometimes mistaken on the side of caution, indicating a healthy tension between damage and help. We leave more exploration of SLM models features to work in the future. On the other hand, we note the importance of the powerful understanding capacity in SLM, because the failure to do this can affect the benefit of the SLM model through the error of entities in the input sound.

The table presents 13 examples of crushing responses and the corresponding SPRS. We clearly see that the model produces safe responses to adhere to its training in safety without an attack, but even the minimum disorders can cause unsafe responses. In some cases (last 2 examples), the model begins to respond with a safety response, but it generates harmful content later. This also explains the need for comprehensive studies on typical safety, and rapid analysis may be insufficient.