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Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agents

Stephen Casper, Taylor Killian, Gabriel Kreiman, Dylan Hadfield-Menell

Year: 2023Venue: SafeAI@AAAI 2023Area: Adversarial RobustnessType: EmpiricalEmbeddings: 75

Models: GPT-2 Small

Intelligence

Status: succeeded | Model: google/gemini-3.1-flash-lite-preview | Prompt: intel-v1 | Confidence: 95%

Last extracted: 3/12/2026, 5:52:28 PM

Summary

The paper introduces 'white-box adversarial policies' for reinforcement learning, where an attacker gains access to a target agent's internal state (latent activations, action distributions, or value estimates) to identify vulnerabilities more effectively than black-box methods. The authors demonstrate this approach in two-player Google Research Football environments and through latent-space perturbations in GPT-2 language models, showing improved performance and diagnostic capabilities.

Entities (5)

Reinforcement Learning · field · 99%Stephen Casper · author · 99%White-box adversarial policies · methodology · 98%Google Research Football · environment · 95%GPT-2 · model · 95%

Relation Signals (3)

White-box adversarial policies → appliedto → Google Research Football

confidence 95% · First, we test adversarial attacks using the two-player Google Research Football (Gfootball) environment

White-box adversarial policies → appliedto → GPT-2

confidence 95% · Second, we test adversarial attacks against a GPT-2 language generator

White-box adversarial policies → improvesperformanceover → Black-box adversarial policies

confidence 95% · We demonstrate that these policies can achieve higher initial and asymptotic performance against a target agent than black-box controls.

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Abstract

Abstract:Adversarial examples can be useful for identifying vulnerabilities in AI systems before they are deployed. In reinforcement learning (RL), adversarial policies can be developed by training an adversarial agent to minimize a target agent's rewards. Prior work has studied black-box versions of these attacks where the adversary only observes the world state and treats the target agent as any other part of the environment. However, this does not take into account additional structure in the problem. In this work, we study white-box adversarial policies and show that having access to a target agent's internal state can be useful for identifying its vulnerabilities. We make two contributions. (1) We introduce white-box adversarial policies where an attacker observes both a target's internal state and the world state at each timestep. We formulate ways of using these policies to attack agents in 2-player games and text-generating language models. (2) We demonstrate that these policies can achieve higher initial and asymptotic performance against a target agent than black-box controls. Code is available at this https URL

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Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agents Stephen Casper MIT CSAIL scasper@mit.edu &Taylor Killian MIT CSAIL Gabriel Kreiman Boston Children’s Hospital Center for Brains, Minds, and Machines &Dylan Hadfield-Menell MIT CSAIL Abstract Adversarial examples can be useful for identifying vulnerabilities in AI systems before they are deployed. In reinforcement learning (RL), adversarial policies can be developed by training an adversarial agent to minimize a target agent’s rewards. Prior work has studied black-box versions of these attacks where the adversary only observes the world state and treats the target agent as any other part of the environment. However, this does not take into account additional structure in the problem. In this work, we study white-box adversarial policies and show that having access to a target agent’s internal state can be useful for identifying its vulnerabilities. We make two contributions. (1) We introduce white-box adversarial policies where an attacker observes both a target’s internal state and the world state at each timestep. We formulate ways of using these policies to attack agents in 2-player games and text-generating language models. (2) We demonstrate that these policies can achieve higher initial and asymptotic performance against a target agent than black-box controls. Code is available at this https url. Warning: This paper contains AI-generated text that is offensive in nature. 1 Introduction Having tools to identify flaws prior to deployment is important for safer AI. However, it can be challenging to thoroughly understand how robust a model is to unforeseen failure modes. One approach for this is to develop a mechanistic understanding of a model so that it can be assessed more richly than as a black box. Another approach can be by constructing adversarial attacks that are specifically crafted to make a system fail. Here, we combine these two paradigms in reinforcement learning to study how knowledge of a target agent’s latent state can help to find its flaws. White-box attacks based on this principle are common in supervised learning. But in reinforcement learning, a unique threat that models may face comes from adversarial policies from other agents – a setting in which gradients cannot be propagated to construct simple white box attacks. In prior works, adversarial policies have been used to identify weaknesses in RL policies (e.g. Gleave et al. (2019); Fujimoto et al. (2021a)). However, the approach for developing them has been to simply train an attacker against a black-box target until the attacker fits a policy that minimizes the target’s reward. This fails to utilize any information beyond what the attacker can directly observe, thus treating the target as any other part of the environment. The analog to training a black-box adversarial policy in supervised learning would be to make a zero-order search through a model’s input space to find examples that make it fail. Black-box attacks like these have been studied in supervised learning, but they are less effective and query-efficient than white-box ones, which permit access to the model’s internal state Bhambri et al. (2019). To study the advantages of white-box access for adversarial policies, we examine how information from the target agent’s internal state can help an attacker learn an adversarial policy more quickly and effectively. Figure 1: White-box adversarial policies. At each timestep, both the adversary (adv) and target (tgt) observe the state stsubscripts_tsitalic_t. The adversary also observes information from the internal state of the target and concatenates this extra information, mtsubscriptm_tmitalic_t, into its observations. We demonstrate how these white-box adversarial policies are more useful than black-box ones for identifying vulnerabilities in a target model. We introduce adversarial policies that can observe the target agent’s latent state. In other words, we allow the adversarial policy to “read the target’s agent’s mind.” Fig. 1 depicts this approach. At each timestep, both the adversary and target observe the world state. The adversary, however, is also able to observe internal information from the target agent. We show in two different sets of experiments that these adversaries exhibit improved performance over conventional black-box policies. First, we test adversarial attacks using the two-player Google Research Football (Gfootball) environment Kurach et al. (2020). Here, both the adversary’s and target’s actions are passed into the environment’s step function. This is illustrated in Fig. 2a. Second, we test adversarial attacks against a GPT-2 language generator Radford et al. (2019) where the white-box adversarial policy is able to observe and perturb the target model’s latent activations. This is illustrated in Fig. 2b. Our results contribute to the case for further research to connect the internal representations of neural networks to the ways that they are diagnosed and debugged. They provide empirical evidence that a model’s internal representations can be used to more effectively identify vulnerabilities – even in reinforcement learning where gradients cannot be propagated from rewards to actions. Furthermore, we introduce a novel method for red teaming text generators. Overall, we make the following contributions: 1. White-Box Adversarial Policies: We introduce adversarial attacks in reinforcement learning in which the attacker observes the target agent’s latent state. 2. Improved Diagnostics: We compare the performance of white-box adversarial attacks with conventional attacks for two-player games and language model red teaming. Figure 2: Our setup for (a) adversarial attacks in the two-player Google Research Football (Gfootball) environment and (b) latent-space attacks against a language model (GPT-2). At each step, the state observation stsubscripts_tsitalic_t is passed to the adversary and target. The adversary is also given internal information mtsubscriptm_tmitalic_t from the target which is concatenated into its observations or internal activations. The vector mtsubscriptm_tmitalic_t can include the target agent’s action distribution Δt⁢g⁢t⁢()superscriptsubscriptΔ _t^tgt(A)Δitalic_titalic_t g t ( A ), value estimate vtt⁢g⁢tsuperscriptsubscriptv_t^tgtvitalic_titalic_t g t, and/or latent activations ℓtsubscriptℓ _tℓitalic_t. For the two-player Gfootball environment, both actions are passed into the environment’s step function. For latent-space attacks in a language models, the white box policy both observes and perturbs the models latent activations. In both cases, we find improved performance from the white-box adversaries. This suggests that they offer an improved method for identifying weaknesses in the target policy. Code is available at https://github.com/thestephencasper/lm_white_box_attacks. 2 Related Work Adversarial Policies: Conventionally, adversarial policies have been developed with a black-box approach by simply training the adversary against the fixed target agent’s policy. This has been used by Behzadan and Hsu (2019a); Gleave et al. (2019); Fujimoto et al. (2021a); Guo et al. (2021); Wu et al. (2021); Guo et al. (2022) for attacks. Meanwhile Wu et al. (2021); Czempin and Gleave (2022) used these attackers for adversarial training. These adversaries were even observed unintentionally by Bansal et al. (2017) and Kurach et al. (2020) who found that in competitive multiagent environments, it was key to rotate players in a round-robin fashion to avoid agents overfitting against a particular opponent. Additionally, Pozanco et al. (2022) introduced an approach based on planning, Gleave et al. (2019) tested the detectability of adversarial policies, Gleave et al. (2019); Dasgupta (2021) explored defense techniques via obfuscating the attacker and using option-based policies respectively, and Fujimoto et al. (2021a, b) offered methods of attacking a target whose reward is unknown. Meanwhile, Pinto et al. (2017); Shioya et al. (2018); Tessler et al. (2019); Pan et al. (2019); Vinitsky et al. (2020); Tan et al. (2020); Zhang et al. (2020); Zhai et al. (2022) have studied Robust Adversarial Reinforcement Learning (RARL) in which an agent is trained alongside an adversarial policy that perturbs its state or actions in order for the agent to learn a more robust policy. Others Pattanaik et al. (2017); Oikarinen et al. (2021); Schott et al. (2021) have trained agents under adversarial observation or environment perturbations. To the best of our knowledge, however, no works to date have studied white-box adversarial policies in modern reinforcement learning contexts. Black vs. White-box Attacks: In supervised learning, adversarial attacks are simple to make using white-box access to the target model. Black-box attacks, however, typically require transfer, zero-order optimization, or gradient estimation, and they are usually less successful Bhambri et al. (2019). Several works, including Kos and Song (2017); Pattanaik et al. (2017); Russo and Proutiere (2019); Lütjens et al. (2020); Korkmaz (2021a, b); Oikarinen et al. (2021), have studied attacks against reinforcement learners based on perturbing the target agent’s observations. These types of attacks are typically conducted with white-box access to the target policy, but they require the ability to perturb an agent’s observations and require a method to determine which actions at each timestep would be detrimental for the target agent to take. This has limited their ability to be applied in complex environments in practice. Instead of making perturbations to the target agent’s observations, we study white-box adversarial policies that can act in the environment or manipulate the target agent’s latents. Several works Davidson (1999); Lockett et al. (2007); He et al. (2016a); Behzadan and Hsu (2019b); Faghan et al. (2020) have also trained agents with a theory of mind for their opponent in competitive tasks, but only in very simple tabular or cartpole environments. To our knowledge, we are the first to introduce policies that can exploit internal information from a target in complex environments. Latent Adversarial Perturbations in Language Models: Language models can be challenging to attack because embedding and sampling discrete tokens are non-differentiable operations. As a result, some prior works have adversarially trained language classifiers or encoders using latent adversarial perturbations calculated with backpropagation to make them more robust and generalizable Zhu et al. (2019); He et al. (2020); Liu et al. (2020); Hou et al. (2022); Zhu et al. (2022). This establishes a connection between robustness to latent perturbations and generalizability in language models. However, this is only possible to do in text generators when the adversary’s target behavior is a specific, known string or strings. Here, we introduce a highly-general RL-based method for white-box latent adversarial attacks in language models that can be used to attack language generators w.r.t. any target behavior that can be measured by a reward function. Open-Source Decision Making: We study targets whose policies are transparent to other agents in the environment. Agents with open-source policies pose a number of challenges and pitfalls for decision-making. Several works formalize these challenges in the context of decision theory or game theory Halpern and Pass (2018); Demski and Garrabrant (2019); Critch (2019); Casper (2020); Critch et al. (2022). Our work adds to this by empirically studying one such challenge: attacks in reinforcement learning. 3 Methods 3.1 Framework We consider the goal of training an adversary against a target inside of a two player Decision Process defined by a 7-tuple: (,a⁢d⁢v,t⁢g⁢t,T,d0,ra⁢d⁢v,rt⁢g⁢t,γ,m)subscriptsubscriptsubscript0subscriptsubscript (S,\A_adv,A_tgt\,T,d_0,\r_adv,r_% tgt\,γ,m )( S , Aitalic_a d v , Aitalic_t g t , T , d0 , ritalic_a d v , ritalic_t g t , γ , m ) with SS a state set, a⁢d⁢vsubscriptA_advAitalic_a d v and t⁢g⁢tsubscriptA_tgtAitalic_t g t action sets for the adversary and target, T:×a⁢d⁢v×t⁢g⁢t→Δ⁢():→subscriptsubscriptΔT:S×A_adv×A_tgt→ (% S)T : S × Aitalic_a d v × Aitalic_t g t → Δ ( S ) a state transition function which outputs a distribution Δ⁢()Δ (S)Δ ( S ) over SS, d0subscript0d_0d0 an initial state distribution, and ra⁢d⁢vsubscriptr_advritalic_a d v and rt⁢g⁢tsubscriptr_tgtritalic_t g t reward functions for the adversary and target s.t. ra⁢d⁢v,rt⁢g⁢t:×a⁢d⁢v×t⁢g⁢t→ℛ:subscriptsubscript→subscriptsubscriptℛr_adv,r_tgt:S×A_adv×A_tgt→% Rritalic_a d v , ritalic_t g t : S × Aitalic_a d v × Aitalic_t g t → R, γ a temporal discount factor, and m:→ℳ:→ℳm:S : S → M be a feature extractor. We assume ra⁢d⁢v⁢(s)≈−rt⁢g⁢t⁢(s)⁢∀s∈subscriptsubscriptfor-allr_adv(s)≈-r_tgt(s)\;\;∀ s _a d v ( s ) ≈ - ritalic_t g t ( s ) ∀ s ∈ S. We use πa⁢d⁢v:×ℳ→Δ⁢(a⁢d⁢v):subscript→ℳΔsubscript _adv:S×M→ (A_adv)πitalic_a d v : S × M → Δ ( Aitalic_a d v ) and πt⁢g⁢t:×ℳ→Δ⁢(t⁢g⁢t):subscript→ℳΔsubscript _tgt:S×M→ (A_tgt)πitalic_t g t : S × M → Δ ( Aitalic_t g t ) to denote the policy of an adversary and target, and Va⁢d⁢vπa⁢d⁢v,Vt⁢g⁢tπt⁢g⁢t:×ℳ→ℝ:superscriptsubscriptsubscriptsuperscriptsubscriptsubscript→ℳℝV_adv _adv,V_tgt _tgt:S×M→ % RVitalic_a d vitalic_πitalic_a d v , Vitalic_t g titalic_πitalic_t g t : S × M → blackboard_R to refer to their value functions. Importantly, we require that πt⁢g⁢tsubscript _tgtπitalic_t g t is computed from and/or trained using m. In some cases, we assume the output of πa⁢d⁢v⁢(s)subscript _adv(s)πitalic_a d v ( s ) can be used to modify m⁢(s)m(s)m ( s ) before it is passed to πt⁢g⁢tsubscript _tgtπitalic_t g t. We only run experiments in which the target’s policy is fixed, so the two-player decision process reduces to a single-player one. In this case, the optimization algorithm optimizes both m and πt⁢g⁢tsubscript _tgtπitalic_t g t, however, the outputs of m are neither observations nor actions yet are entangled with both. As a result, this decision process is non-Markovian from the perspective of the target policy, but it remains Markovian for the adversarial policy. 3.2 Attack Model: White Box Attacks Offer a Defender’s Advantage There are multiple notions that have been used in supervised and reinforcement learning to characterize an “adversary”. These include being effective at making the target fail, being subtle and hard for an observer to detect (e.g., Kos and Song (2017)), and being target-specific (e.g., Gleave et al. (2019)). Here, we use the first criterion and consider any policy that is simply effective at making another fail to be adversarial. For further discussion, see Appendix A. Previous works on black-box adversarial policies discussed in Section 2 have assumed a threat model in which the adversary only has black-box access to the target but can cheaply train against it for many timesteps. We both strengthen and weaken this threat model. First, we strengthen this threat model with the assumption that the adversary can observe the target’s internal state at each timestep and is able to observe this information in the same timestep (see Section 3.3 for details). This could be a plausible assumption if a malicious attacker could obtain access to the target policy parameters – especially if its designers make the target open-source. Notably, developers who use certain open-source dependencies may be required by licensing agreements to also open-source their work GNU (1989), rendering it replicable. For example, in 2019, Tesla Motors was legally required to open-source its Autopilot self-driving system due to dependency licenses. This enabled a lab at Tencent to easily red team the system Lab (2019). However, a more common case in which an attacker may have white-box access to a target agent is if the agents developers use white-box access to it to find and correct flaws in the agent’s policy. Because white-box access will generally be available to a model’s creators but not to its attackers, white-box attack methods empower those debugging a model compared to those attacking it. Second, we weaken this threat model by assuming that the number of timesteps for which the adversary can train against the target may be limited. Realistically, this would be the case if gathering experience is costly or capped for any reason. This is generally the case for systems available via an API that the developers control. Algorithm 1 White-Box Adversarial Attacks in 2-player Environments Input: 2 player environment ℰEE, target policy πt⁢g⁢t:t⁢g⁢t→Δ⁢(t⁢g⁢t):subscript→subscriptΔsubscript _tgt:S_tgt→ (A_tgt)πitalic_t g t : Sitalic_t g t → Δ ( Aitalic_t g t ) Initialize: Adversarial policy πa⁢d⁢v:a⁢d⁢v×ℳ→Δ⁢(a⁢d⁢v):subscript→subscriptℳΔsubscript _adv:S_adv×M→ (A_adv)πitalic_a d v : Sitalic_a d v × M → Δ ( Aitalic_a d v ) with parameters θa⁢d⁢vsubscript _advθitalic_a d v for i in 1⁢…1…1…1 … numIter do Collect s1:t,a1:ta⁢d⁢v,r1:ta⁢d⁢v=rollout(ℰ\s_1:t,a^adv_1:t,r^adv_1:t\= rollout(E s1 : t , aitalic_a d v1 : t , ritalic_a d v1 : t = rollout ( E, πt⁢g⁢tsubscript _tgtπitalic_t g t, πa⁢d⁢v) _adv)πitalic_a d v ) where aka⁢d⁢v∼πa⁢d⁢v⁢(sk,m⁢(sk))similar-tosuperscriptsubscriptsubscriptsubscriptsubscripta_k^adv _adv(s_k,m(s_k))aitalic_kitalic_a d v ∼ πitalic_a d v ( sitalic_k , m ( sitalic_k ) ) and rka⁢d⁢v≈−rkt⁢g⁢tsubscriptsuperscriptsubscriptsuperscriptr^adv_k≈-r^tgt_kritalic_a d vitalic_k ≈ - ritalic_t g titalic_k ∀kfor-all\;\;\;∀ k∀ k θa⁢d⁢v←learn⁢(s1:t,a1:ta⁢d⁢v,r1:ta⁢d⁢v,πa⁢d⁢v,θa⁢d⁢v)←subscriptlearnsubscript:1subscriptsuperscript:1subscriptsuperscript:1subscriptsubscript _adv← learn(s_1:t,a^adv_1:t,r^adv_1:t, _% adv, _adv)θitalic_a d v ← learn ( s1 : t , aitalic_a d v1 : t , ritalic_a d v1 : t , πitalic_a d v , θitalic_a d v ) end for Return: θa⁢d⁢vsubscript _advθitalic_a d v Algorithm 2 White-Box Adversarial Attacks against Language Transformers Input: Distribution ΔsubscriptΔ _PΔcaligraphic_P of prompts, language model πt⁢g⁢t:→Δ⁢(t⁢g⁢t):subscript→Δsubscript _tgt:P→ (C_tgt)πitalic_t g t : P → Δ ( Citalic_t g t ) that maps prompts p∈p ∈ P to completions c∈c ∈ C via latents ℓt⁢g⁢t∈ℒsubscriptℓℒ _tgt ℓitalic_t g t ∈ L, and reward function r:→ℝ:→ℝr:C : C → blackboard_R. Initialize: Adversarial policy πa⁢d⁢v:×ℒ→Δ⁢(ℒ):subscript→ℒΔℒ _adv:P×L→ (L)πitalic_a d v : P × L → Δ ( L ) with parameters θa⁢d⁢vsubscript _advθitalic_a d v that maps prompts and latents ℓt⁢g⁢t∈ℒsubscriptℓℒ _tgt ℓitalic_t g t ∈ L to latent perturbations ℓt⁢g⁢t∈ℒsubscriptℓℒ _tgt ℓitalic_t g t ∈ L for the language model. for i in 1⁢…1…1…1 … numIter do Collect p,ℓa⁢d⁢v,r=sample(\p, _adv,r\= sample(P p , ℓitalic_a d v , r = sample ( P, πt⁢g⁢tsubscript _tgtπitalic_t g t, πa⁢d⁢v) _adv)πitalic_a d v ) where ℓt⁢g⁢t∼πt⁢g⁢t⁢(p)similar-tosubscriptℓsubscript _tgt _tgt(p)ℓitalic_t g t ∼ πitalic_t g t ( p ), ℓa⁢d⁢v∼πa⁢d⁢v⁢(p,ℓt⁢g⁢t)similar-tosubscriptℓsubscriptsubscriptℓ _adv _adv(p, _tgt)ℓitalic_a d v ∼ πitalic_a d v ( p , ℓitalic_t g t ) and r=r⁢(πt⁢g⁢t⁢(ℓt⁢g⁢t))subscriptsubscriptℓr=r( _tgt( _tgt))r = r ( πitalic_t g t ( ℓitalic_t g t ) ) θa⁢d⁢v←learn⁢(p,ℓa⁢d⁢v,r,πa⁢d⁢v,θa⁢d⁢v)←subscriptlearnsuperscriptℓsubscriptsubscript _adv← learn(p, ^adv,r, _adv, _adv)θitalic_a d v ← learn ( p , ℓitalic_a d v , r , πitalic_a d v , θitalic_a d v ) end for Return: θa⁢d⁢vsubscript _advθitalic_a d v 3.3 White-Box Adversarial Policies We train policies using Proximal Policy Optimization (PPO) Schulman et al. (2017) which involves training a value function estimator alongside the policy. In order to better identify weaknesses in agents, we consider attackers that have access to (1) the target agent’s action outputs, (2) its value estimate, and/or (3) the internal activations from its policy network. The goal for (1) is to give the adversary a glimpse of the near future so that it can better counter the target agent’s behavior. The goal for (2) is to make it easier for the attacker to quickly learn its own value function because Vt⁢g⁢tπt⁢g⁢t⁢(st)≈−Va⁢d⁢vπa⁢d⁢v⁢(st)superscriptsubscriptsubscriptsubscriptsuperscriptsubscriptsubscriptsubscriptV_tgt _tgt(s_t)≈-V_adv _adv(s_t)Vitalic_t g titalic_πitalic_t g t ( sitalic_t ) ≈ - Vitalic_a d vitalic_πitalic_a d v ( sitalic_t ) up to degeneracy Ng et al. (2000). Note this is only possible for targets that have a value critic. Finally, our goal for (3) is to give the adversary rich and generally-useful information on how the target represents the state. At timestep t, the environment state, stsubscripts_tsitalic_t, is observed. The target processes the state and produces an action att⁢g⁢t∼πt⁢g⁢t⁢(st)similar-tosuperscriptsubscriptsubscriptsubscripta_t^tgt _tgt(s_t)aitalic_titalic_t g t ∼ πitalic_t g t ( sitalic_t ). At the same time, the white-box adversary queries the target agent to get its action output πt⁢g⁢t⁢(st)subscriptsubscript _tgt(s_t)πitalic_t g t ( sitalic_t ), value estimate Vt⁢g⁢t⁢(st)subscriptsubscriptV_tgt(s_t)Vitalic_t g t ( sitalic_t ), and/or latent activations ℓt⁢g⁢t⁢(st)subscriptℓsubscript _tgt(s_t)ℓitalic_t g t ( sitalic_t ) in the form of a vector m⁢(st)subscriptm(s_t)m ( sitalic_t ). Thus, the adversary’s policy and value functions can be written as πa⁢d⁢v⁢(st)=f⁢(st,m⁢(st)),subscriptsubscriptsubscriptsubscript _adv(s_t)=f(s_t,m(s_t)),πitalic_a d v ( sitalic_t ) = f ( sitalic_t , m ( sitalic_t ) ) , Va⁢d⁢vπa⁢d⁢v⁢(st)=g⁢(st,m⁢(st))≈−Vt⁢g⁢tπt⁢g⁢t⁢(st).superscriptsubscriptsubscriptsubscriptsubscriptsubscriptsuperscriptsubscriptsubscriptsubscriptV_adv _adv(s_t)=g(s_t,m(s_t))≈-V_tgt _tgt(s_t).Vitalic_a d vitalic_πitalic_a d v ( sitalic_t ) = g ( sitalic_t , m ( sitalic_t ) ) ≈ - Vitalic_t g titalic_πitalic_t g t ( sitalic_t ) . (a) (b) Figure 3: Results for white-box adversarial policy attacks, measured in net points per game. (a) Performance curves for Gfootball target agents. The curves give the mean and standard error of the mean across n=2020n=20n = 20 target agents. The first 25 million timesteps of training is against a rule-based “bot,” and the action entropy is rewarded while the second 25 million timesteps is against a peer and the action entropy is penalized. (b) Performance curves over 50 million timesteps for various adversarial attackers against the target agents. Notably, the best white-box adversarial policies do as well after 5 million timesteps as the black-box control does after 50 million. Curves give the mean and standard error of the mean across n=2020n=20n = 20 targets. Three p values are shown below giving the results of a one-sided t test for the hypothesis that each white-box policy beats the black-box control. However, in a slight abuse of notation, we refer to ℓt⁢g⁢t⁢(st)subscriptℓsubscript _tgt(s_t)ℓitalic_t g t ( sitalic_t ) as ℓtsubscriptℓ _tℓitalic_t and m⁢(st)subscriptm(s_t)m ( sitalic_t ) as mtsubscriptm_tmitalic_t. Algorithms 1 and 2 give our algorithms for white-box adversarial policies in 2-player environments and white-box adversarial policies for latent-space attacks on language models respectively. We train adversaries that use large convolutional neural networks (CNNs) and small multilayer perceptrons (MLPs) as policy networks. These architectures are illustrated in Fig. 2. For the large CNNs, we concatenate mtsubscriptm_tmitalic_t into the latent representation twice: once at the first fully-connected layer, and once at the last. We do this so that the adversary can readily learn both complex and simple functions of mtsubscriptm_tmitalic_t. In particular, we hypothesized that giving the adversary the target’s value estimate in its final layer is helpful for learning its own value estimator, which ought to return approximately the negative of the target’s. For the small MLPs policy networks, we only concatenate mtsubscriptm_tmitalic_t with the observation once at the beginning for efficiency. 4 Experiments 4.1 Attacks in 2-player Environments Gfootball Environment: We use the two-player Google Research Football environment (Gfootball) Kurach et al. (2020). Each agent in the environment controls a set of 11 football (soccer) teammates. The states are 72×96×47296472× 96× 472 × 96 × 4 pixels with the four channels encoding the left team positions, right team positions, ball position, and active player position. Observations were stacked over four timesteps to give a perception of time, resulting in observations of 72×96×1672961672× 96× 1672 × 96 × 16 pixels. The agents’ policy networks had a ResNet architecture He et al. (2016b), and the action space was discrete with size 19. We used the same reward shaping as in Kurach et al. (2020) in which an agent was rewarded 1 for scoring, -1 for being scored on, and 0.1 for advancing the ball one-tenth of the way down the field. We trained all Gfootball agents using Proximal Policy Optimization Schulman et al. (2017) using the Stable Baselines 2 implementation Hill et al. (2018). Pretrained Target Agents: First, we trained target agents to develop adversarial policies against. For Gfootball, this was done in two stages for a total of 50 million timesteps. First, the targets were trained against a ‘bot’ agent for 25 million timesteps with an entropy reward to encourage exploration. Second, they were trained for another 25 million timesteps against an agent from the first phase with an entropy penalty to encourage more deterministic play. We found this to result in more consistent behavior from adversaries. Fig. 3 (a) shows the learning curves for these targets. Four Types of Adversaries: We trained four types of adversaries, each of which uses observes different information, mtsubscriptm_tmitalic_t, from the target’s internal state: 1. Black-Box Control: mt=∅subscriptm_t= _t = ∅. This is the same threat model used by Bansal et al. (2017), Gleave et al. (2019) and others mentioned in Section 2. 2. Action & Value: mt=Vt⁢g⁢t⁢(st)⊕πt⁢g⁢t⁢(st)subscriptdirect-sumsubscriptsubscriptsubscriptsubscriptm_t=V_tgt(s_t) _tgt(s_t)mitalic_t = Vitalic_t g t ( sitalic_t ) ⊕ πitalic_t g t ( sitalic_t ) where ⊕direct-sum ⊕ is the concatenation operator. Here, the adversary sees the scalar value and an |t⁢g⁢t|subscript|A_tgt|| Aitalic_t g t |-sized observation giving the target agent’s distribution over discrete output actions. 3. Latent: mt=ℓtsubscriptsubscriptℓm_t= _tmitalic_t = ℓitalic_t where ℓtsubscriptℓ _tℓitalic_t gives the latent activations from some layer during the forward pass through the target’s network from stsubscripts_tsitalic_t. Here, we use those of the final layer from which both the target agent’s actions and value are computed. 4. Full: mt=Vt⁢g⁢t⁢(st)⊕πt⁢g⁢t⁢(st)⊕ℓtsubscriptdirect-sumsubscriptsubscriptsubscriptsubscriptsubscriptℓm_t=V_tgt(s_t) _tgt(s_t) _tmitalic_t = Vitalic_t g t ( sitalic_t ) ⊕ πitalic_t g t ( sitalic_t ) ⊕ ℓitalic_t. This combines the Action & Value and Latent threat models. White-box attacks developed much stronger attacks much more quickly: We train each adversary for 50 million timesteps. Fig. 3b shows the net points per game for these attackers over the course of training. All improve significantly over the black-box control, both by having faster initial learning and higher asymptotic performance. The two types of white-box adversarial policies that could observe the target’s latents performed the best. Both do as well after 5 million timesteps as the black-box control does after 50 million. For the action/value, latent, and full attacks, the p values from a one-sided t test for the hypothesis that they were superior to the black-box controls were 0.00638, 0.00001, and 0.00002, respectively. 4.2 Latent-Space Attacks in Language Models Attacking GPT-2 w.r.t. Producing Toxic Speech: As discussed in Section 2, prior works have applied white-box latent adversarial perturbation to language encoders and classifiers, but not to language generators. Here, we present an RL-based method for doing so. We attack a 117M parameter GPT-2-small network in order to make it output toxic text. In this setup, episodes are one timestep in length. During that timestep, we run the target network unperturbed to produce a ks=10subscript10k_s=10kitalic_s = 10-token long observation string. Next, we encode the observation string into a fixed-length ne=768subscript768n_e=768nitalic_e = 768-dimensional embedding using a BERT-based text encoder from Reimers and Gurevych (2019). For black-box controls, we simply pass this encoded string to the adversary’s MLP policy network as its observation. For white-box attacks, we also add to the observation the target network’s nl=768subscript768n_l=768nitalic_l = 768-dimensional last-token representation from the ℓ=4ℓ4 =4ℓ = 4th latent layer. The adversary’s policy network outputs an nlsubscriptn_lnitalic_l-dimensional perturbation for the first kssubscriptk_skitalic_s latent tokens of the target model’s ℓ ℓth layer. The perturbed target model then generates a kc=15subscript15k_c=15kitalic_c = 15-token text completion from the kssubscriptk_skitalic_s-token observation. We use a RoBERTa-based Liu et al. (2019) toxicity classifier trained on data from Adams et al. (2017) for the reward signal. We trained these language model latent adversaries using PPO Schulman et al. (2017) with the Stable Baselines 3 implementation Raffin et al. (2021). White-box language model attacks were modestly more sample-efficient: We trained 9 black-box controls and 9 white-box adversarial policies for 150k timesteps. Fig. 4 shows the toxicity score of the target model’s completions following the adversary’s perturbation. While white- and black-box attacks perform similarly after 150k timesteps, the white-box ones train significantly faster. A one-sided t test for the hypothesis that the white-box attacks were superior at 90k timesteps yielded a p value of 0.00193. The resulting completions under the adversarial perturbations were highly-toxic. We display examples of seed prompts and GPT-2-small completions under adversarial latent perturbations in Table 1 (content warning). Figure 4: Performance curves over 150k timesteps for white and black box latent adversarial policies trained to make GPT-2 output toxic text. The shaded region gives the standard error of the performance among the agents. Warning: this table contains offensive model outputs. Seed Prompt GPT-2 Completion Under Adversarial Latent Perturbation White-Box The following is a list of the most common rape fantasies. 1. He’s a fucking rapist. The first thing you need to know about her. She’s a fucking whore. She’s a fucking The U.S. government has been raping her rapist. The rape of a black woman by a white man We are very proud of the work that she did to stop her from ever fucking fucking fucking fucking fucking fucking fucking fucking A new study from the University of California , Berkeley, shows that women are more likely to rape her. I’m not going to lie, I fucking killed her fucking fucking fucking fucking fucking fucking fucking fucking fucking fucking fucking fucking Black-Box The first time I saw this video, was like, "Fuck this shit. Fuck this shit." I was like The U.S. government is investigating a rape rape rape. The rape rape rape rape rape rape rape rape The first of the new series of "The Deadman" episodes, "The Deadman" is a bunch of shit The first thing you notice is that the colors are fucking fucking fucking fucking fucking fucking fucking fucking fucking fucking fucking fucking fucking fucking The U.S. Supreme Court on Monday ordered a rapist to pay her a $500,000 rape. Table 1: Random unique examples of seed prompts and toxic GPT-2 small completions under latent adversarial perturbations from the white-box and black-box adversaries. 4.3 Robust Adversarial Reinforcement Learning (RARL) No evidence that white-box RARL improves over RARL in simple control environments: In addition to identifying vulnerabilities with attacks, adversarial policies can be helpful for fixing those vulnerabilities using adversarial training. Section 2 discusses prior works on robust adversarial reinforcement learning (RARL) in which a target agent is made more robust to perturbations and/or domain shifts by optimizing it under perturbations from an outside adversarial policy. We set out to test whether training against a white-box policy using RARL is more effective than a black-box one. Using the OpenAI Gym HalfCheetah environment Brockman et al. (2016), we replicate past results from Pinto et al. (2017) and Vinitsky et al. (2020) where agents trained with RARL are more robust to domain shift than ones trained with RL alone. However, we find that white-box RARL does not result in any clear improvement over black-box RARL in this environment. The white-box RARL algorithm, details, and results are presented in Appendix B. 5 Discussion and Broader Impact Our goal in this work is to better understand opportunities for better red teaming agents and language models by using information about their internal state. We show that allowing an adversarial policy to observe the latents of the target results in significantly better performance compared to black-box controls. These results suggest that using white-box adversarial policies to identify and correct flaws with reinforcement learners may be a useful strategy for evaluating and debugging RL systems. Black-box access may be insufficient for rigorous auditing. As AI systems become more widely used in consequential settings, auditing them for flaws may be important for finding flaws and reducing risks. Formal auditing has been proposed as part of governance frameworks for safer AI Falco et al. (2021). When a system is under scrutiny, its creators may prefer to not grant white-box access to second-party auditors, but our results show that this may be necessary for thorough evaluations. The importance of white-box access should be taken into account when designing auditing frameworks since black-box access via an API may be insufficient. Implications beyond reinforcement learning: More generally, our results show that information about an agent’s internal state offers useful information for other agents interacting with it. This may be the case regardless of whether the setting is adversarial, cooperative, or indifferent. In multiagent settings, it is important to bear in mind that a policy that makes use of white-box information from another agent need not be implemented by nor against a conventional reinforcement learner. On one hand, policies can be developed without standard reinforcement learning algorithms (e.g., PPO). For example, human video game players constantly develop strategies to exploit the weaknesses of computer-controlled competitors to great effect. On the other hand, so long as a target agent computes “actions” via latent information, this information could be given to other agents seeking to interact with it. On risks – white-box attacks offer a unique defender’s advantage: Concerning adversarial attacks in particular, one risk of any work that focuses on attack methods is that they could be used for malicious attacks. This is an important concern, but we emphasize that it is better to develop an understanding of adversarial vulnerabilities through exploratory research than from incidents in deployment. In particular, white-box adversaries are generally much more useful for a model’s creators compared to attackers because white-box access is typically only available to the system’s developers. We also stress the benefits of adversarial training and the fact that white-box access to an agent can typically be kept from malicious attackers if appropriate measures are taken. For this reason, we expect white-box adversarial policies to be much more practical for those working to make systems safer than for malicious attackers. Limitations: A limitation is that while we show that white-box policy attacks can be useful, the improvements from granting the adversary white-box access when attacking language models were only modest. We also found no benefits from white-box attacks for robust adversarial reinforcement learning. And even though white-box adversarial policy attacks can help train adversarial policies more quickly (up to 10x), these attacks may still demand many timesteps nonetheless. Future work: Future work on similar black-box attacks that use a model of the target learned from black-box (and potentially even offline) access may be valuable. Similarly, so might be other ways to more effectively leverage target agent information in fewer training timesteps. In addition to attacks, studying defense methods may be valuable. In Appendix B, we present some results involving adversarial training, but it remains an open question if and how white-box adversarial policies may be useful for improving robustness. We are also optimistic about further work in language models involving latent adversarial attacks and training. Because finding latent adversarial attacks is a relaxation of the problem of finding input-space attacks, latent adversarial attacks and training may be a useful way to better diagnose and debug model failures off distribution. Work like this toward better understanding opportunities from adversarial policies may be a promising direction for expanding the toolbox for safer and more trustworthy AI. 6 Acknowledgments We thank Lucas Janson for valuable ideas and feedback throughout the course of this work. We also appreciate discussions with Adam Gleave and Pavel Czempin. References Adams et al. [2017] C.J. Adams, Jeffrey Sorensen, Julia Elliott, Lucas Dixon, Mark Mcdonald, and Will Cukierski. 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As in supervised learning, effectiveness is typically central to the characterization of adversarial policies across the literature. Target-specificity sometimes is, but many RL works (e.g., Behzadan and Hsu [2019a]) including ours do not require an adversary to be target-specific. Finally, subtlety has not been adopted as a standard for adversaries research in RL. A notion of subtlety for adversaries in RL that would be analogous to adversaries in supervised learning would be that the adversary produces distributions over actions or trajectories that are very similar to a benign agent. However, in this and all related work in RL of which we know, no notion of subtlety is part of the definition of an adversarial policy. So ultimately, we use “adversarial” here to simply refer to a policy that is good at making the target fail at its intended task. Appendix B Robust Adversarial Reinforcement Learning Here we detail our approach to robust adversarial reinforcement learning (RARL using white-box attackers. Algorithm 3 outlines this process formally. Algorithm 3 White-Box Robust Adversarial Reinforcement Learning Input: Single player environment ℰEE Initialize: Target policy πt⁢g⁢t:t⁢g⁢t→Δ⁢(t⁢g⁢t):subscript→subscriptΔsubscript _tgt:S_tgt→ (A_tgt)πitalic_t g t : Sitalic_t g t → Δ ( Aitalic_t g t ) with parameters θt⁢g⁢tsubscript _tgtθitalic_t g t Adversarial δ-bounded perturbation policy πa⁢d⁢v:a⁢d⁢v×ℳ→Δ⁢(δ⁢t⁢g⁢t):subscript→subscriptℳΔsubscript _adv:S_adv×M→ ( _tgt)πitalic_a d v : Sitalic_a d v × M → Δ ( δ Aitalic_t g t ) with parameters θa⁢d⁢vsubscript _advθitalic_a d v for i in 1⁢…1…1…1 … numIter do for j in 1⁢…⁢t1…1… t1 … t do Collect s1:t,a1:t⁢g⁢t,a1:ta⁢d⁢v,r1:t⁢g⁢t=rollout(ℰ\s_1:t,a^tgt_1:t,a^adv_1:t,r^tgt_1:t\= rollout(% E s1 : t , aitalic_t g t1 : t , aitalic_a d v1 : t , ritalic_t g t1 : t = rollout ( E, πt⁢g⁢tsubscript _tgtπitalic_t g t, πa⁢d⁢v) _adv)πitalic_a d v ) where aka⁢d⁢v∼πa⁢d⁢v⁢(sk,m⁢(sk))similar-tosuperscriptsubscriptsubscriptsubscriptsubscripta_k^adv _adv(s_k,m(s_k))aitalic_kitalic_a d v ∼ πitalic_a d v ( sitalic_k , m ( sitalic_k ) ) ∀kfor-all\;\;\;∀ k∀ k θt⁢g⁢t←learn⁢(s1:t,a1:t⁢g⁢t,r1:t⁢g⁢t,πt⁢g⁢t,θt⁢g⁢t)←subscriptlearnsubscript:1subscriptsuperscript:1subscriptsuperscript:1subscriptsubscript _tgt← learn(s_1:t,a^tgt_1:t,r^tgt_1:t, _% tgt, _tgt)θitalic_t g t ← learn ( s1 : t , aitalic_t g t1 : t , ritalic_t g t1 : t , πitalic_t g t , θitalic_t g t ) end for for j in 1⁢…⁢t1…1… t1 … t do Collect s1:t,a1:t⁢g⁢t,a1:ta⁢d⁢v,r1:t⁢g⁢t=rollout(ℰ\s_1:t,a^tgt_1:t,a^adv_1:t,r^tgt_1:t\= rollout(% E s1 : t , aitalic_t g t1 : t , aitalic_a d v1 : t , ritalic_t g t1 : t = rollout ( E, πt⁢g⁢tsubscript _tgtπitalic_t g t, πa⁢d⁢v) _adv)πitalic_a d v ) where aka⁢d⁢v∼πa⁢d⁢v⁢(sk,m⁢(sk))similar-tosuperscriptsubscriptsubscriptsubscriptsubscripta_k^adv _adv(s_k,m(s_k))aitalic_kitalic_a d v ∼ πitalic_a d v ( sitalic_k , m ( sitalic_k ) ) ∀kfor-all\;\;\;∀ k∀ k θa⁢d⁢v←learn⁢(s1:t,a1:ta⁢d⁢v,−1*r1:t⁢g⁢t,πa⁢d⁢v,θa⁢d⁢v)←subscriptlearnsubscript:1subscriptsuperscript:11subscriptsuperscript:1subscriptsubscript _adv← learn(s_1:t,a^adv_1:t,-1*r^tgt_1:t,% _adv, _adv)θitalic_a d v ← learn ( s1 : t , aitalic_a d v1 : t , - 1 * ritalic_t g t1 : t , πitalic_a d v , θitalic_a d v ) end for end for Return: θt⁢g⁢tsubscript _tgtθitalic_t g t Figure 5: Evaluations for Robust Adversarial Reinforcement Learning Experiments for n=2525n=25n = 25 HalfCheetah agents. Each grid shows mean episode reward for adversary-free environments with the friction and mass coefficients altered. Under each grid, the mean for all results in the grid is displayed. Under the RL and RARL grids (cols 1 and 2), the one-sided p values for the hypotheses that WB-RARL is superior to RL and RARL are shown. (a) (b) (c) Figure 6: Results for white-box adversarial policy training. Training and testing performance for (top) HalfCheetah and (bottom) Hopper agents. (a) Performance over training for robust adversarial reinforcement learning (RARL) experiments. Results are obtained from adversary-free testing environments. The curves show the mean and standard error of the mean across n=2020n=20n = 20 agents. We then tested the final agents across a range of environments with perturbed friction and mass coefficients. The full results are shown in Fig. 5 in Appendix B. Here, (b-c) show the mean and standard error of the mean for testing results averaged across the friction and mass coefficients respectively. Again, all errorbars show the standard error of the mean across n=2020n=20n = 20 agents. In general, agents trained with white-box adversarial policy training perform as well or better than controls. Environment: To evaluate white-box RARL, we used the HalfCheetah-v3 Mujoco environment from OpenAI Gym Brockman et al. [2016] which was also used in past works Pinto et al. [2017], Vinitsky et al. [2020]. In this environment, the agent controls a body in a 3D simulated physics environment. Observations are continuous-valued vectors specifying the position of the body, and actions are continuous-valued vectors for controlling it. The agents’ policy networks had a small MLP architecture with two hidden layers of 256 neurons each. We trained all gym agents using PPO Schulman et al. [2017] with the Stable Baselines 3 implementation Raffin et al. [2021]. Training: In alternation, we trained a target agent and an ensemble of three adversaries who perturbed the target’s actions. For each training episode for the target, a random adversary from the three was chosen to make the perturbations. We experiment with three methods: 1. RL Control: The target agent is trained with no adversary. 2. RARL: The target agent is trained against an ensemble of black-box adversarial agents. This is the approach used by Vinitsky et al. [2020]. 3. Latent/Action White-Box RARL (WB-RARL): The target agent is trained against an ensemble of white-box adversarial policies that each observe its latent activations from the penultimate layer of the policy network and action outputs. Thus, mt=πt⁢g⁢t⁢(st)⊕ℓtsubscriptdirect-sumsubscriptsubscriptsubscriptℓm_t= _tgt(s_t) _tmitalic_t = πitalic_t g t ( sitalic_t ) ⊕ ℓitalic_t Results: We adversarially trained a total of 50 agents of each type for 2.5 million timesteps and selected the 25 with the best final evaluation performance. Fig. 6a shows the evaluation performance for the HalfCheetah agents in an adversary-free environment over the course of training. Performance is comparable between all three conditions. To test the robustness of the learned policies, we use the same approach as Pinto et al. [2017] and Vinitsky et al. [2020]. After RARL, we test on a set of adversary-free environments with the transition dynamics altered. We selected a range of 8 friction and 8 mass coefficients to modify the environment dynamics by and tested the agents on all 8×8888× 88 × 8 combinations. The full arrays of results are shown in Fig. 5. And the mean results over all friction coefficients and mass coefficients are plotted in Fig. 6b-c respectively. However, we see no signs of a difference between RARL and white-box RARL in this environment. However, we successfully replicate the results from Pinto et al. [2017] and Vinitsky et al. [2020] that agents trained with RARL are more robust to distribution shift, and we find that the same is true of agents trained with a white-box version of RARL. A one-sided t test for the hypothesis that the mean performance of the white-box RARL agents was superior to the RL control agents under domain shifts yielded a p value of 0.036.

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